Optimal Resolution Research Articles

Large-field high-resolution X-ray AKB microscope for measuring hydrodynamic instabilities at the SG-III prototype laser facility.

X-ray imaging with a large field of view (FOV) and high resolution is extremely important for Rayleigh-Taylor instability measurement with a small amplitude and high spatial frequency in laser inertial confinement fusion. We developed an advanced Kirkpatrick-Baez (AKB) microscope based on the quadratic-aberration theory to realize a large FOV and high resolution. This microscope was assembled and tested in a laboratory, and it was then successfully applied for imaging the hydrodynamic instability of a perturbation target in implosion experiments at the Shenguang-III prototype laser facility. Imaging results demonstrate that the AKB microscope can achieve an optimal resolution of ~ 0.53μm and ~ 0.40μm and a spatial resolution of < 1.5μm within a 300-µm FOV and < 4.5μm in a 1-mm FOV.

Rapid and objective assessment of auditory temporal processing using dynamic amplitude-modulated stimuli.

Current tests of hearing fail to diagnose pathologies in ~10% of patients seeking help for hearing difficulties. Neural ensemble responses to perceptually relevant cues in the amplitude envelope, termed envelope following responses (EFR), hold promise as an objective diagnostic tool to probe these 'hidden' hearing difficulties. But clinical translation is impeded by current measurement approaches involving static amplitude modulated (AM) tones, which are time-consuming and lack optimal spectrotemporal resolution. Here we develop a framework to rapidly measure EFRs using dynamically varying AMs combined with spectrally specific analyses. These analyses offer 5x improvement in time and 30x improvement in spectrotemporal resolution, and more generally, are optimal for analyzing time-varying signals with known spectral trajectories of interest. We validate this approach across several mammalian species, including humans, and demonstrate robust responses that are highly correlated with traditional static EFRs. Our analytic technique facilitates rapid and objective neural assessment of temporal processing throughout the brain that can be applied to track auditory neurodegeneration using EFRs, as well as tracking recovery after therapeutic interventions.

Identification of Cactus Species Using the MobileNet Convolutional Neural Network Model

The objective of this design is to create a system capable of accurately and precisely classifying different types of succulent cacti. The system design aims to create a succulent cactus classification application using a Convolutional Neural Network (CNN) with the MobileNet architecture. The process involves collecting cactus images, dividing them into training and test datasets, and developing a CNN model to recognize patterns in succulent cactus species. The training data is used to train the CNN model, and the test data evaluates the model's accuracy. The trained model, stored in Tf.lite format, successfully classifies 15 cactus types, achieving high accuracy by employing preprocessing steps like resizing, normalization, and background removal. Over 1,200 cactus images were taken with a smartphone, categorized into 15 classes, and prepared to ensure optimal lighting, angle, background, and resolution (224x224 pixels). The MobileNet model was chosen for its high accuracy and efficiency. Hardware used includes a Samsung A54 smartphone and an Intel i7 laptop, with software such as Python, Kotlin, and Android Studio facilitating development. This design ensures the application’s accessibility, making it a valuable tool for cactus enthusiasts and the general public to easily identify different succulent cactus types. Testing of the cactus species classification program using the Convolutional Neural Network (CNN) method with the MobileNetV2 architecture yielded strong results, achieving over 90% accuracy in classifying 15 cactus species. The highest training accuracy of 0.9837 was achieved at 150 epochs without early stopping, outperforming other epoch configurations. The model successfully classified species across five main genera—Kalanchoe, Crassula, Echeveria, Haworthia, and Euphorbia. This high accuracy highlights the model's effectiveness, making it a useful tool for cactus enthusiasts and the public to accurately identify and distinguish cactus species.

Enhancing offshore parcel delivery efficiency through vessel-unmanned aerial vehicle collaborative routing

Offshore oil and gas production is vital for global energy supply, but it faces logistical challenges due to the high costs and inefficiencies of traditional supply methods. This paper introduces the vessel-unmanned aerial vehicle (UAV) routing problem with multiple visits in a single flight (VURP-M), a novel logistical model that addresses aimed at enhancing the replenishment of offshore platforms with small, essential items. The VURP-M allows the UAV to perform multiple visits during a single flight, optimising the delivery process. To tackle the VURP-M, we propose two mixed-integer second-order cone programs that capture the problem's complexities. Given its NP-hard nature, we employ an adaptive large neighbourhood search (ALNS) method, featuring a segmented initialisation process and problem-specific operators guided by a rule-based mechanism to improve solution efficiency. The ALNS formulates an initial solution by solving a travelling salesman problem to create a giant tour, which is then used to group sequential targets into multiple UAV flights. The subsequent optimal resolution of a SOCP determines the take-off and landing points for each flight. Subsequently, the ALNS refines the initial solution through destroy and repair operators, enhancing the search for superior sequences and allocation schemes. The effectiveness of our approach is demonstrated through a real-world case study and numerical experiments on random instances featuring up to 100 platforms. The results offer implications of the collaborative vessel-UAV model for the offshore logistics industry.

Low-Field MRI for Dental Imaging in Pediatric Patients With Supernumerary and Ectopic Teeth: A Comparative Study of 0.55 T and Ultra-Low-Dose CT.

This study sought to elucidate the diagnostic performance of 0.55 T magnetic resonance imaging (MRI) for pediatric dental imaging, specifically in terms of the image quality (IQ) for detecting ectopic and/or supernumerary teeth, compared with routine ultra-low-dose computed tomography (ULD-CT) of the jaw. A total of 16 pediatric patients (mean age: 12.4 ± 2.6 years, range: 9-17 years) with ectopic and/or supernumerary teeth screened from January 2023 to January 2024 were enrolled in this prospective, single-center study. All patients underwent ULD-CT as the clinical reference standard and 0.55 T MRI as the study scan on the same day. A 0.6-mm isotropic 3-dimensional T1w FLASH sequence was developed with a dedicated field of view of the upper and lower jaws. ULD-CT was performed using a new single-source computed tomography (CT) scanner equipped with a tin filter (Sn100, slice thickness: 1 mm, quality reference mAs: 24). The IQ for the tooth axis, the tooth length, the tooth root, root resorptions, cysts, the periodontal ligament space, and the mandibular canal was evaluated twice by 3 senior readers using a 5-point Likert scale (LS) (LS score of 1: insufficient, 3: reduced IQ but sufficient for clinical use, and 5: perfect) and compared between both methods. Subsequently, the results were dichotomized into nonvalid (LS score of ≤2) and valid (LS score of ≥3) for clinical use. A total of 49 ectopic and/or supernumerary teeth in 16 pediatric patients were investigated using ULD-CT (CTDI: 0.43 ± 0.09 mGy) and 0.55 T MRI. The mean MRI acquisition time was 9:45 minutes. Motion artifacts were nonsignificantly different between 0.55 T MRI and ULD-CT (P = 0.126). The IQ for the tooth axis, the tooth root, root resorptions, and cysts was similar between the methods. The IQ for the periodontal ligament space and tooth length favored ULD-CT by 14% (confidence interval [CI]: 4.3%-24%) and 7.5% (CI: 1.8%-13%), respectively, whereas that for the mandibular canal favored 0.55 T MRI by -35% (CI: -54%-16%). Sufficient IQ was found especially for cystic lesions (CT: 100% sufficient, MRI: 95% sufficient), the tooth root (CT: 100%, MRI: 98%), root resorptions (CT: 94%; MRI: 85%), the tooth axis (CT: 100%; MRI: 98%), and the tooth length (CT: 99%; MRI: 91%). The findings indicate that 0.55 T MRI is a feasible, radiation-free technique for delineating ectopic and/or supernumerary teeth in pediatric patients. Nevertheless, to date, 0.55 T MRI has not yet been able to provide an optimal IQ for all anatomical tooth and jaw structures. In cases of advanced clinical indications that require optimal spatial resolution, high-resolution CT or cone-beam CT may still be necessary.

Prediction on Yellowfin Tuna (Thunnus albacares) Fishing Ground in Waters Near the Marshall Islands Based on SMOTETomek‐RF

ABSTRACTThis study monitored 37 longliners fishing in waters near the Marshall Islands from 2020 to 2022 by Liancheng Overseas Fishery (Shenzhen) Co., Ltd.'s operation management system. This study developed nine predictive models on the relationship between catch per unit effort (CPUE) data for yellowfin tuna (Thunnus albacares) and the environmental data. The environmental data integrate 48 variables, including eddy kinetic energy, chlorophyll a concentration, sea surface height, and additional measures of vertical oceanic conditions, alongside spatiotemporal parameters (year, month, day, longitude, and latitude). This study employed four spatial resolutions (0.25° × 0.25°, 0.5° × 0.5°, 1° × 1°, and 2° × 2°) to develop nine predictive models: KNN, RF, GBDT, CART, LightGBM, XGBoost, CatBoost, AdaBoost, and Stacking (RF, KNN, GBDT, and LR). These models, with a daily time resolution, were trained using 75% of the data and tested with the remaining 25%. The optimal spatial resolution and model were determined through a comprehensive comparison of model evaluation metrics across these spatial resolutions. The SMOTETomek algorithm was then applied to resample 75% of the data at the optimal spatial resolution, forming a new training dataset. This dataset was used to refine the model, subsequently tested with the remaining 25% of the data. Results indicated that (1) the optimal spatial resolution is 0.25° × 0.25° and the optimal model is RF; (2) the SMOTETomek algorithm enhances the model's predictive performance; and (3) the developed SMK‐RF model, exhibiting Acc and AUC values of 76.73% and 82.47%, respectively, accurately predicts the central fishing grounds for yellowfin tuna, consisting closely with actual fishing activity.

Rapid Whole-Plate Cell and Tissue Micropatterning Using a Budget 3D Resin Printer.

The ability to precisely pattern cells and proteins is crucial in various scientific disciplines, including cell biology, bioengineering, and materials chemistry. Current techniques, such as microcontact stamping, 3D bioprinting, and direct photopatterning, have limitations in terms of cost, versatility, and throughput. In this Article, we present an accessible approach that combines the throughput of photomask systems with the versatility of programmable light patterning using a low-cost consumer LCD resin printer. The method involves utilizing a bioinert hydrogel, poly(ethylene glycol) diacrylate (PEGDA), and a 405 nm sensitive photoinitiator (LAP) that are selectively cross-linked to form a hydrogel upon light exposure, creating specific regions that are protein and cell-repellent. Our result highlights that a low-cost LCD resin printer can project virtual photomasks onto the hydrogel, allowing for reasonable resolution and large-area printing at a fraction of the cost of traditional systems. The study demonstrates the calibration of exposure times for optimal resolution and accuracy and shape corrections to overcome the inherent challenges of wide-field resin printing. The potential of this approach is validated through widely studied 2D and 3D stem cell applications, showcasing its biocompatibility and ability to replicate complex tissue engineering patterns. We also validate the method with a cell-adhesive polymer (gelatin methacrylate; GelMA). The combination of low cost, high throughput, and accessibility makes this method broadly applicable across fields for enabling rapid and precise fabrication of cells and tissues in standard laboratory culture vessels.

New high-precision measurement system for electron–positron pairs from sub-GeV/GeV gamma-rays in the emulsion telescope

The GRAINE project observes cosmic gamma-rays, using a balloon-borne emulsion-film-based telescope in the sub-GeV/GeV energy band. We reported in our previous balloon experiment in 2018, GRAINE2018, the detection of the known brightest source, Vela pulsar, with the highest angular resolution ever reported in an energy range of >80MeV. However, the emulsion scanning system used in the experiment was designed to achieve a high-speed scanning, and it was not accurate enough to ensure the optimum spatial resolution of the emulsion film and limited the performance. Here, we report a new high-precision scanning system that can be used to greatly improve the observation result of GRAINE2018 and also be employed in future experiments. The scanning system involves a new algorithm that recognizes each silver grain on an emulsion film and is capable of measuring tracks with a positional resolution for the passing points of tracks of almost the same as the intrinsic resolution of nuclear emulsion film (∼70 nm). This resolution is approximately one order of magnitude smaller than that obtained with the high-speed scanning system. With this scanning system, an angular resolution for gamma-rays of 0.1° at 1GeV is expected to be achieved. Furthermore, we successfully combine the new high-precision scanning system with the existing high-speed scanning system, enabling the high-speed and high-precision measurements. Employing these techniques, we reanalyze the gamma-ray events detected previously by only the high-speed scanning system in GRAINE2018 and obtain an about three times higher angular resolution (0.22°) in the 500–700MeV energy range. Adopting this technique in future observations may provide new insights into the gamma-ray emission from the Galactic center region and may realize polarization measurements of high-energy cosmic gamma-rays.

The Power of Reagent Titration in Flow Cytometry.

Flow cytometry facilitates the detection of multiple cell parameters simultaneously with a high level of resolution and throughput, enabling in-depth immunological evaluations. High data resolution in flow cytometry depends on multiple factors, including the concentration of reagents used in the staining protocol, and reagent validation and titration should be the first step in any assay optimization. Titration is the process of finding the concentration of the reagent that best resolves a positive signal from the background, with the saturation of all binding sites, and minimal antibody excess. The titration process involves the evaluation of serial reagent dilutions in cells expressing the antigen target for the tested antibody. The concentration of antibody that provides the highest signal to noise ratio is calculated by plotting the percentage of positive cells and the intensity of the fluorescence of the stained cells with respect to the negative events, in a concentration-response curve. The determination of the optimal antibody concentration is necessary to ensure reliable and reproducible results and is required for each sample type, reagent clone and lot, as well as the methods used for cell collection, staining, and storage conditions. If the antibody dilution is too low, the signal will be too weak to be accurately determined, leading to suboptimal data resolution, high variability across measurements, and the underestimation of the frequency of cells expressing a specific marker. The use of excess antibodies could lead to non-specific binding, reagent misuse, and detector overloading with the signal off scale and higher spillover spreading. In this publication, we summarized the titration fundamentals and best practices, and evaluated the impact of using a different instrument, sample, staining, acquisition, and analysis conditions in the selection of the optimal titer and population resolution.

Method Development and Validation of Stability-Indicating HPLC-UV Method for Determination of 5 -Fluorouracil

Introduction: In the realm of pharmaceutical analysis, the development and validation of a High-Performance Liquid Chromatography with Ultraviolet Detection (HPLC-UV) method for the precise determination of 5-FU stand as a crucial endeavor. Methods: This study outlines the systematic process of method development, optimization, and subsequent validation to ensure the reliability and applicability of the method in pharmaceutical quality control. Method optimization included the selection of an appropriate stationary phase, mobile phase composition, and chromatographic conditions to achieve optimum resolution, sensi-tivity, and run time. Results: A speedy, simple, and dependable HPLC technique for quantifying 5-FU on a C18 (250 4.6 mm, 5m particle size) column with solvent A included 10 mM phosphate buffer with pH 3.0 and solvent B with acetonitrile as the mobile phase. At 250 nm, the effluent was measured. Fur-ther, 5-FU had a retention time of 1.698 minutes. Conclusion: According to ICH criteria, the devised technique was validated for precision, accu-racy, linearity range, robustness, and ruggedness.

Optical signal characteristics analysis of atmospheric disturbance density fields generated by high-speed aircraft

Aircraft disturbs the adjacent atmospheric environment in flight, forming spatial distribution features of atmospheric density that differ from the natural background, which may potentially be utilized as tracer characteristics to introduce new technologies for indirectly sensing the presence of aircraft. In this paper, the concept of a long-range aircraft detection based on the atmospheric disturbance density field is proposed, and the detection mode of tomographic imaging of the scattering light of an atmospheric disturbance flow field is designed. By modeling the spatial distribution of the disturbance density field, the scattered echo signal images of active light towards the disturbance field at long distance are simulated. On this basis, the characteristics of the disturbance optical signal at the optimal detection resolution are analyzed. The results show that the atmospheric disturbance flow field of the supersonic aircraft presents circular in the light-scattering echo images. The disturbance signal can be further highlighted by differential processing of the adjacent scattering images. As the distance behind the aircraft increases, the diffusion range of the disturbance signal increases, and the signal intensity and contrast with the background decrease. Under the ground-based observation conditions of the aircraft at a height of 10000 m, a Mach number of 1.6, and a detection distance of 100 km, the contrast between the disturbance signal and the background was 30 dB at a distance of one time from the rear of the fuselage, and the diffusion diameter of the disturbance signal was 50 m. At a distance eight times the length of the aircraft, the contrast decreased to 10 dB, and the diameter increased to 290 m. The contrast was reduced to 3 dB at a distance nine times the length of the aircraft, and the diameter was diffused to 310 m. These results indicate the possibility of long-range aircraft detection based on the characteristics of the atmospheric density field.

Development and performance evaluation of a thin GAGG:Ce scintillator plate for high resolution synchrotron radiation X-ray imaging

Scintillator-based X-ray imaging detectors are pivotal in numerous scientific and practical domains, including medical imaging, food and device inspection, and security monitoring. Recent advancements have spurred interest in 4D X-ray imaging using synchrotron radiation, necessitating higher temporal resolutions. Consequently, this places stringent demands on X-ray detector technology, especially when X-ray energy exceeds 20 keV. The selection of a suitable scintillator material is crucial for achieving optimal timing resolution, yet it poses a significant challenge in dynamic X-ray imaging. This study delves into the optimization of scintillator properties and their impact on spatial resolution and light output, elucidating the performance of Ce-doped Gd3Ga3Al2O12 (GAGG:Ce) scintillators for X-ray imaging applications. We developed a micro X-ray imaging detector using a 100 μm-thick GAGG:Ce scintillator plate and conducted X-ray imaging tests at the Aichi SR facility. The results demonstrated that the resolution, quantified as the chart slit width at a contrast transfer function (CTF) value of 10%, reached 2 ∼ 3 μm with a 4× lens, 0.52 μm± 0.03 μm with a 20× lens, and 0.42 μm± 0.01 μm with a 40× lens. Although the results of this study did not achieve a spatial resolution nearing the effective pixel size of the 40× lens, the text also elucidates the underlying reasons for this limitation. Furthermore, we compared the X-ray sensitivity of our GAGG:Ce scintillator plate with that of a commercial LuAG:Ce scintillator, revealing an approximately 1.5-fold increase in light output. As a demonstration, transmission images of dried small fish were captured using the GAGG:Ce scintillator plate and the developed X-ray imaging system. These findings highlight the potential of the X-ray imaging detector devised in this study for future generations of X-ray imaging applications.

A deep learning framework for automated anomaly detection and localization in fused filament fabrication

Under-extrusion poses a common challenge in filament 3D printing, necessitating precise adjustments to printing parameters for optimal resolution. Swift identification of this flaw is crucial for implementing timely corrective measures. In response, we present a novel machine learning approach designed to detect anomalies in 3D printing, specifically in the fused filament fabrication process (FFF). Our framework utilizes “You Only Look Once” (YOLO), a real-time object detection system, and “Visual Geometry Group-16” (VGG-16), a Convolutional Neural Network (CNN) model for image recognition, to accurately identify and localize under-extrusion events. Initially, models such as VGG-16, VGG-19, and ResNet-50 were trained without the inclusion of YOLO to establish baseline accuracies. Subsequently, an automated image pre-processing phase employs YOLO to discern the nozzle head, facilitating subsequent cropping around the region of interest for retraining the models. This inclusion of the nozzle head detection approach significantly improved the accuracy of all models, with the combined application of YOLO and VGG-16 demonstrating the most substantial enhancement, boosting detection accuracy to 97%. Moreover, Gradient-weighted Class Activation Mapping (Grad-CAM) technique, another CNN-based method, is employed to effectively highlight predicted areas in under-extrusion scenarios. While our method significantly advances the automatic detection of printing anomalies, it primarily serves as a diagnostic tool, signaling the need for intervention. Our rigorous testing on images of varying complexities confirms the model’s robustness, evaluated using comprehensive metrics such as precision, recall, and F1 score.

입법과 사법의 경계: 법률해석의 한계

What is the relationship between legislation and adjudication? Traditionally, it is believed that judges should simply apply the law to specific facts and render judgments accordingly. Montesquieu encapsulated this view by describing judges as "the mouthpieces of the law." In contrast, the Free Law Movement posits that judges are not strictly bound by the text of the law and have the freedom to discover or even create law as needed. Between these two extremes, there exists a range of perspectives on the relationship between legislative and judicial powers. This relationship is crucial because it defines the limits of legal interpretation, making it a central issue in judicial decision-making. This paper begins by exploring the objectives and methods of legal interpretation. It then examines how the Supreme Court of Korea, particularly through its en banc decisions, navigates the relationship between legislation and judicial precedent, or between legislative theory and interpretive theory. The Supreme Court cases discussed in this paper — covering topics such as the gender correction for transgender individuals, parental status of children born through artificial insemination, nominee trusts and unconscionable legal transactions, special limited approval for minor heirs, and judicial reduction of penalty clauses — all address challenging issues that sit at the intersection of legislation and adjudication. The judiciary is vested with the authority to interpret the law, determine its meaning, define its scope, and apply it to specific cases. However, this authority is not without limits. Judges must interpret laws in a manner that respects the legislative power to create laws and must avoid distorting, altering, or replacing the legislator' s decision that can be ascertained from relevant statutory text. This approach is consistent with the constitutional principles of separation of powers and the rule of law, which delineate the distinct roles of the legislative and judicial branches. A legislator's legislative decision cannot be overturned by a judge's judicial decision. Judges must also fulfill their constitutional duty to adjudicate independently according to their conscience (Article 103 of the Constitution of Republic of Korea). It is the judge's role to ascertain the just meaning of the law, grounded in the Constitution and statutes, and to apply it to individual cases. When interpreting and applying laws, judges must confront the critical question of whether to prioritize the intent of the legislature, adhere strictly to the text of the law, or pursue the broader objectives of the law. This question forms both the starting and ending points of legal interpretation and influences views on the boundary between legislative and judicial functions. Whether there has been a legislative decision is a question of the boundaries of statutory interpretation. Legislative solutions, within the constitutional framework, offer the advantage of providing comprehensive resolutions and selecting the best option among various alternatives. However, it is not always possible or appropriate to defer to legislative solutions. The judiciary must, therefore, seek the optimal resolution within the boundaries established by the Constitution and statutes, guided by judicial conscience.

Feedback mechanisms controlling Antarctic glacial-cycle dynamics simulated with a coupled ice sheet–solid Earth model

Abstract. The dynamics of the ice sheets on glacial timescales are highly controlled by interactions with the solid Earth, i.e., the glacial isostatic adjustment (GIA). Particularly at marine ice sheets, competing feedback mechanisms govern the migration of the ice sheet's grounding line (GL) and hence the ice sheet stability. For this study, we developed a coupling scheme and performed a suite of coupled ice sheet–solid Earth simulations over the last two glacial cycles. To represent ice sheet dynamics we apply the Parallel Ice Sheet Model (PISM), and to represent the solid Earth response we apply the 3D VIscoelastic Lithosphere and MAntle model (VILMA), which, in addition to load deformation and rotation changes, considers the gravitationally consistent redistribution of water (the sea-level equation). We decided on an offline coupling between the two model components. By convergence of trajectories of the Antarctic Ice Sheet deglaciation we determine optimal coupling time step and spatial resolution of the GIA model and compare patterns of inferred relative sea-level change since the Last Glacial Maximum with the results from previous studies. With our coupling setup we evaluate the relevance of feedback mechanisms for the glaciation and deglaciation phases in Antarctica considering different 3D Earth structures resulting in a range of load-response timescales. For rather long timescales, in a glacial climate associated with the far-field sea-level low stand, we find GL advance up to the edge of the continental shelf mainly in West Antarctica, dominated by a self-amplifying GIA feedback, which we call the “forebulge feedback”. For the much shorter timescale of deglaciation, dominated by the marine ice sheet instability, our simulations suggest that the stabilizing sea-level feedback can significantly slow down GL retreat in the Ross sector, which is dominated by a very weak Earth structure (i.e., low mantle viscosity and thin lithosphere). This delaying effect prevents a Holocene GL retreat beyond its present-day position, which is discussed in the scientific community and supported by observational evidence at the Siple Coast and by previous model simulations. The applied coupled framework, PISM–VILMA, allows for defining restart states to run multiple sensitivity simulations from. It can be easily implemented in Earth system models (ESMs) and provides the tools to gain a better understanding of ice sheet stability on glacial timescales as well as in a warmer future climate.

UAV or satellites? How to find the balance between efficiency and accuracy in above ground biomass estimation of artificial young coniferous forest?

The accurate estimation of Above Ground Biomass (AGB) is the basis for plantation forest carbon trading. This study focused on Picea crassifolia artificial plantations, extracting individual tree crown diameters and heights using Unmanned Aerial Vehicles (UAV) data and calculating the individual tree biomass using allometric growth equations. These results were then used to train a satellite image AGB prediction model. In additional, satellite images were resampled to different resolutions to assess the impact of satellite image resolution on model the accuracy. Finally, the model with the highest accuracy among the deep learning algorithms was selected to predicts the AGB within the P. crassifolia plantation forest. The results indicated that the accuracy of single tree crown diameters extracted from P. crassifolia point clouds significantly surpassed those extracted from general point clouds and Crown Height Model (CHM), while the accuracy of the heights extracted from all three sources was similar; RepLKNet outperformed GoogLeNet and ResNet in identifying plantation forest; random forest slightly outperformed XGBoost in the capability of AGB prediction, while the accuracy of the AGB prediction models initially increasd and then decreasd with satellite image resolution, reaching the highest accuracy at a resolution of 50 m. This indicates that the optimal satellite image resolution for estimating the AGB in the study area was affected by scale effects of 50 m. Compared with the combination of satellite data and manual field measurements, the concurrent use of UAVs and satellites offers significant advantages in terms of efficiency and accuracy. UAVs can replace manual sampling for carbon sequestration transactions for plantations.

A Novel Method for Development and Validation of the Degradation Products Analysis of N-Carbamylglutamate with UHPLC by Using Design of Experiment Approach.

Carglumic acid, also known as N-carbamyl-l-glutamic acid, is a medication used in the treatment of a rare genetic disorder called N-acetylglutamate synthase (NAGS) deficiency. To the authors' knowledge, there was no method reported in the literature for the determination of degradation products suitable for quality control analyses of carglumic acid. Thus, the aim of the presented study is to develop an impurity method with a UHPLC/DAD detector configuration compatible with industrial standards from the European Pharmacopeia and the United States Pharmacopeia, making the drug more accessible for developing and underdeveloped countries through its precise evaluation. The method involved the separation of carglumic acid and its degradation products using a reverse-phase C18 column (Waters, BEH 150 mm × 2.1 mm, 1.7 μm) at a flow rate of 0.39 mL/min with a stop time of 10 min. To separate all unknown and known impurities, a gradient elution (phosphate buffer, pH 2.4, and acetonitrile) system was used. The detection was performed at 214 nm. Forced degradation studies were conducted under different stress conditions, including acidic, basic, oxidative, thermal, and photolytic stress. Placket-Burman statistical experimental design was used to demonstrate the robustness of this method, and the suitability of the method was confirmed under the applied conditions. Box-Behnken design was used to provide the optimum resolution between the peaks determined to be critical during the optimization. The developed method was validated according to ICH guidelines for specificity, linearity, accuracy, precision, and robustness. The limit of detection and limit of quantification were 0.7 and 0.15 μg/mL for carglumic acid, respectively.

Regulating emulsion properties through tannin acid-mediated gelatin/cellulose nanocrystal complexes: From low-oil emulsion to high internal phase emulsion gel for 3D printing

In this study, a stabilized low-oil emulsion and high internal phase emulsion gels (HIPE gels) integrated preparation method is proposed. Highly stable low-oil emulsions, prepared using tight ternary complexes formed by incorporating tannic acid (TA) into gelatin/cellulose nanocrystals (GLT/CNC) complexes mainly through strengthening hydrogen-bonding interaction. The subsequent heating-centrifugation process was used to enable the conversion of GLT/CNC/TA stabilized low-oil emulsions to stabilized HIPE gels, and systematically investigated the regulation of emulsion properties by TA concentrations. The ternary complexes formed by appropriate concentration of TA (0.6 %) can form stable low-oil emulsions with smallest average particle (36.1 ± 0.25 μm) size and best rheological properties, while the rheological properties of HIPE gels were similarly regulated by the TA concentration (0 %, 0.2 %, 0.6 %, 1.0 %, 1.2 %). Heating induced flocculation of low-oil emulsions without causing demulsification, allowed for the subsequent centrifugation to yield stable HIPE gels, significantly superior to the GLT/CNC/TA-stabilized HIPE gels prepared by conventional methods that typically exhibit thermal instability. TA effectively promoted the interfacial adsorption of GLT/CNC and tight stacking of oil droplets to build a more stable network structure in the continuous phase. HIPE gels with 0.6 % TA demonstrated excellent 3D printability with optimal dimensional resolution and surface quality, presenting a novel strategy for HIPE gel preparation and versatile emulsion applications.

Optimizing Additive Manufacturable Structures with Computer Vision to Enhance Material Efficiency and Structural Stability

This study introduces an innovative technique that merges computer vision with topology optimization to advance additive manufacturing. Employing advanced photogrammetry software, we obtain high-resolution images of the design domain, which are then used to develop accurate 3D models through meticulous scanning procedures. These models are transformed into an STL file format and remeshed using an adaptive algorithm within COMSOL 5.3 Multiphysics, facilitated by a custom MATLAB 2023 application. This integration achieves the optimal mesh resolution and precision in analytical assessments. We applied this technique to the design of a concrete pillar for 3D printing, targeting a 75% reduction in volume to improve the material efficiency and structural stability—critical factors for extraterrestrial applications. The design, captured with a 360-degree camera array, guided the MATLAB-based topology optimization process. By combining MATLAB’s optimization algorithms with COMSOL’s meshing and finite element analysis tools, we investigated various material-efficient configurations. The findings reveal a substantial volume reduction, especially in the central region of the design, effectively optimizing material utilization while preserving structural integrity. The optimization algorithm exhibited a swift and stable convergence, reaching near-optimal solutions within approximately 20 iterations.

Hydrological Connectivity Response of Typical Soil and Water Conservation Measures Based on SIMulated Water Erosion Model: A Case Study of Tongshuang Watershed in the Black Soil Region of Northeast China

The black soil region of Northeast China is the largest commercial grain production base in China, accounting for about 25% of the total in China. In this region, the water erosion is prominent, which seriously threatens China’s food security. It is of great significance to effectively identify the erosion-prone points for the prevention and control of soil erosion on the slope of the black soil region in Northeast China. This article takes the Tongshuang small watershed (Heilongjiang Province in China) as an example, which is dominated by hilly landforms with mainly black soil and terraces planted with corn and soybeans. Based on the 2.5 cm resolution Digital Elevation Model (DEM) reconstructed by unmanned aerial vehicles (UAVs), we explore the optimal resolution for hydrological simulation research on sloping farmland in the black soil region of Northeast China and explore the critical water depth at which erosion damage occurs in ridges on this basis. The results show that the following: (1) Compared with the 2 m resolution DEM, the interpretation accuracy of field roads, wasteland, damaged points, ridges and cultivated land at the 0.2 m resolution is increased by 4.55–27.94%, which is the best resolution in the study region. (2) When the water depth is between 0.335 and 0.359 m, there is a potential erosion risk of ridges. When the average water depth per unit length is between 0.0040 and 0.0045, the ridge is in the critical range for its breaking, and when the average water depth per unit length is less than the critical range, ridge erosion damage occurs. (3) When local erosion damage occurs, the connectivity will change abruptly, and the remarkable change in the index of connectivity (IC) can provide a reference for predicting erosion damage.