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In the present study, polycaprolactone (PCL) composite films incorporated with various concentrations of grapefruit seed extract (GSE) as an antimicrobial agent were prepared using a twin-screw extruder. Physical characteristics as well as antimicrobial properties of the PCL/GSE composite films were analyzed. The results showed that the surface color of the films gradually changed with increasing GSE concentration. Fourier transform infrared spectra indicated no significant structural changes such as chemical bond formation between PCL and GSE. Thermal properties were slightly affected due to GSE incorporation. Crystallinity of the composite films decreased as the amount of GSE increased. In vitro analysis indicated that the antimicrobial activity of the PCL/GSE composite films increased as the GSE concentration increased, with a 5% concentration showing the strongest inhibitory activity against Listeria monocytogenes , with 5.8-log reduction in bacterial count. Application testing of the films was carried out for cheese packaging, and biodegradation of the samples was assessed via soil burial testing. Our findings confirmed the potential use of PCL/GSE composite films as biodegradable food packaging material with antimicrobial activity.

Mung bean starch (MBS)-based edible films with incorporation of guar gum (GG) and sunflower seed oil (SSO) were developed in this study. MBS, GG, and SSO were used as the main filmogenic biopolymer, thickener, and hydrophobicity-imparting substance, respectively. To investigate the effect of SSO content on the physicochemical, mechanical, and optical properties of the films, they were supplemented with various concentrations (0, 0.5, 1, and 2%, w/w) of SSO. Increasing SSO content tended to decrease tensile strength, elongation at break, crystallinity, water solubility, and the water vapor permeability; in contrast, it increased the oxygen transmission rate and water contact angle. Consequently, the incorporation of SSO into the matrix of MBS-based films decreased their mechanical strength but effectively enhanced their water-resistance properties. Therefore, the MBS-based film developed here can be properly used as an edible film in settings that require high water-resistance properties but do not call for robust mechanical strength.

Patients who complete neoadjuvant chemotherapy for breast cancer without a pathological complete response have a high risk of relapse. A randomized trial comparing capecitabine with no additional adjuvant therapy showed that capecitabine prolonged disease-free and overall survival. Patients who have residual invasive breast cancer after the receipt of neoadjuvant chemotherapy have a high risk of relapse. 1 The rate of complete response as assessed on pathological testing (hereafter, pathological complete response) ranges from 13 to 22% among patients with human epidermal growth factor receptor 2 (HER2)–negative primary breast cancer. 1 Patients who do not have a pathological complete response after the receipt of neoadjuvant taxane and anthracycline chemotherapy have a 20 to 30% risk of relapse. 2 Patients with HER2-negative cancer who receive neoadjuvant chemotherapy often receive postoperative radiation therapy, whereas endocrine therapy is administered to patients with hormone-receptor–positive disease . . .

This article presents an efficient method for isolating cellulose nanocrystals (CNcs) from seaweed waste using a combination of electron beam (E-beam) irradiation and acid hydrolysis. This approach not only reduces the chemical consumption and processing time, but also improves the crystallinity and yield of the CNcs. The isolated CNcs were then thermally annealed at 800 and 1000 °C to produce porous nanocarbon materials, which were characterized using scanning electron microscopy, X-ray diffraction, Raman spectroscopy, and X-ray photoelectron spectroscopy to assess their structural and chemical properties. Electrochemical testing of electrical double-layer capacitors demonstrated that nanocarbon materials derived from seaweed waste-derived CNcs annealed at 1000 exhibited superior capacitance and stability. This performance is attributed to the formation of a highly ordered graphitic structure with a mesoporous architecture, which facilitates efficient ion transport and enhanced electrolyte accessibility. These findings underscore the potential of seaweed waste-derived nanocarbon as a sustainable and high-performance material for energy storage applications, offering a promising alternative to conventional carbon sources.

Alcohol use disorder (AUD) is a chronic disease with a higher recurrence rate than that of other mental illnesses. Moreover, it requires continuous outpatient treatment for the patient to maintain abstinence. However, with a low probability of these patients to continue outpatient treatment, predicting and managing patients who might discontinue treatment becomes necessary. Accordingly, we developed a machine learning (ML) algorithm to predict which the risk of patients dropping out of outpatient treatment schemes. A total of 839 patients were selected out of 2,206 patients admitted for AUD in three hospitals under the Catholic Central Medical Center in Korea. We implemented six ML models-logistic regression, support vector machine, k-nearest neighbor, random forest, neural network, and AdaBoost-and compared the prediction performances thereof. Among the six models, AdaBoost was selected as the final model for recommended use owing to its area under the receiver operating characteristic curve (AUROC) of 0.72. The four variables affecting the prediction based on feature importance were the length of hospitalization, age, residential area, and diabetes. An ML algorithm was developed herein to predict the risk of patients with AUD in Korea discontinuing outpatient treatment. By testing and validating various machine learning models, we determined the best performing model, AdaBoost, as the final model for recommended use. Using this model, clinicians can manage patients with high risks of discontinuing treatment and establish patient-specific treatment strategies. Therefore, our model can potentially enable patients with AUD to successfully complete their treatments by identifying them before they can drop out.

Shoulder disorders, including frozen shoulder resulting from stroke-induced hemiplegia, significantly reduce a patient’s ability to perform activities of daily living, thereby necessitating repeated rehabilitation. Consequently, extensive research has been conducted on rehabilitation robots to assist in upper-limb motor recovery. The shoulder moves according to the scapulohumeral rhythm. Considering the biomechanical characteristics of the shoulder joint, the rehabilitation robot was designed to replicate a similar kinematic environment using actuators and linkages that emulate the structures of the upper arm, shoulder, and clavicle. To ensure precise operation, the kinematic accuracy of the robot was pre-evaluated. Kinematic analyses were conducted using MATLAB, and the results were compared with coordinate data from the mechanical design to evaluate positional accuracy. In addition, the convergence and accuracy of joint-angle estimation for target positions were analyzed. The forward kinematic analysis revealed that the average positional error between the measured and target coordinates ranged from 0.5% to 2.8%, with the Base Motor–Back Motor segment exhibiting the highest error (2.8%). The inverse kinematic analysis demonstrated stable convergence to the target positions through iterative computations using the Gauss–Newton method, confirming that the actual motion could be accurately reproduced within the designed range of motion.

This study evaluates land cover classification accuracy through adjustments to the deep learning model (DLM) training process, including variations in loss function, the learning rate scheduler, and the optimizer, along with diverse input dataset compositions. DLM datasets were created by integrating surface reflectance (SR) spectral data from satellite imagery with textural information derived from the gray-level co-occurrence matrix, yielding four distinct datasets. The U-Net model served as the baseline, with models A and B configured by adjusting the training parameters. Eight land cover classifications were generated from four datasets and two deep learning training conditions. Model B, utilizing a dataset comprising spectral, textural, and terrain information, achieved the highest overall accuracy of 90.3% and a kappa coefficient of 0.78. Comparing different dataset compositions, incorporating textural and terrain data alongside SR from satellite imagery significantly enhanced classification accuracy. Furthermore, using a combination of multiple loss functions or dynamically adjusting the learning rate effectively mitigated overfitting issues, enhancing land cover classification accuracy compared to using a single loss function.

The extra-articular distal humerus plate (EADHP) has been widely used for surgical treatment of distal humerus shaft fracture (DHSF). However, the surgical approach, fixation methods, and implant positions of the EADHP remain controversial owing to iatrogenic radial nerve injury and complaints such as skin irritation related to the plate. Anterior plating with a modified (upside-down application) proximal humerus locking plate (PHILOS) has been proposed as an alternative, However, research on its biomechanical performance remain insufficient and were mostly based on retrospective studies. This study quantitatively compared and evaluated the biomechanical performance between posterior plating with the EADHP and anterior plating with a modified PHILOS using finite element analysis (FEA). The FEA simulation results that both the EADHP and PHILOS had adequate biomechanical performance and stability under axial, bending, and varus force load conditions. The PHILOS has a fixed stability comparable to that of the EADHP, and fixation was achieved using only four locking screws within a fixed range of 30 mm just above the olecranon fossa. The results show that the PHILOS could be an option for the fixation of a DHSF when considering the dissection range and complaints (e.g. skin irritation) associated with the EADHP.

A smart thinning operation refers to an advanced method of selecting and cutting trees to be thinned based on digitally captured forest information. In smart thinning operations, workers use the coordinates of individual trees to navigate to the target trees for thinning. However, it is difficult to accurately locate individual trees in a forest stand covered with a canopy, necessitating a precise real-time positioning system that can be used in the forest. Therefore, this study aimed to evaluate the applicability of the global navigation satellite system real-time kinematic (GNSS-RTK) device in a forest stand through analysis of its positioning accuracy within the forest environment and evaluation of the operational range of the single-baseline RTK based on analysis of the positioning precision and radio signal strength index (RSSI) change with increasing distance from the base station. The results showed that the root mean square error (RMSE) of the horizontal positioning error was highly accurate, with an average of 0.26 m in Larix kaempferi stands and 0.48 m in Pinus koraiensis stands. The RSSI decreased to a minimum of −103.3 dBm within 1 km of distance from the base station; however, this had no significant impact on the horizontal positioning precision. The conclusion is that the GNSS-RTK is suitable for use in smart thinning operations.

The effects of different nutrient solution quantities on growth, physicochemical, nutritional, and postharvest qualities of lettuce were investigated. Two differently pigmented Korean leaf lettuce cultivars “Geockchima” and “Cheongchima” were grown in soilless perlite culture supplied with 250, 500, 1000, and 2000 mL·d−1·plant−1 nutrient solutions. Several growth parameters (plant height, leaf number, fresh weight, dry matter) were evaluated. The highest lettuce growth was observed when plants were supplied with 1000 mL·d−1·plant−1. Cultivating lettuces in the lowest nutrient solution quantity showed higher dry matter, crude fiber, osmolality, chlorophyll, and anthocyanin contents. Upon increasing the nutrient solution, the crispiness, greenness, and levels of ascorbic acid, nitrogen, and potassium, increased, while phosphorus and magnesium were unaffected, and calcium content declined. Postharvest qualities were better maintained in lettuces irrigated with the least amount of nutrient solution, extending their shelf life. We conclude that lettuce can be grown with 1000 mL·d−1·plant−1 for higher yield, and short-term storage and/or transportation. However, when lettuces need to be stored for a certain period, such as long-distance shipment, they should be cultivated with a limited nutrient solution, which requires further detailed investigation. The results of this study can be applied for distributing, storing, transporting, and marketing lettuce.