In this study, We aim to provide design data for a low-temperature refrigeration system to select operating conditions for predicting maximum performance of an eco-friendly binary refrigeration system based on changing operating conditions The operating variables considered in this paper are evaporating temperature, condensing temperature, superheating degree, subcooling degree, and compression efficiency. The main results are summarized as follows: In the low temperature range of -50℃ to -30℃, the COP of the system increased as the evaporating temperature and subcooling degree of the binary refrigeration system for R744-R717 increased, but the COP decreased as the condensing temperature and superheating degree increased. It was confirmed that factors such as superheating, subcooling, condensing temperature, evaporating temperature, cascade temperature difference, and compression efficiency affect the performance coefficient of the binary refrigeration cycle for R744 and R717, and it was found that each of these factors has a cascade evaporating temperature that maximizes the performance of the binary refrigeration cycle.

It is addressed that the challenges of poor cyclic stability and low conductivity in metal–organic frameworks (MOFs) hinder their application in energy storage. Here, we synthesized binary metal MOFs through a one-step hydrothermal process, subsequently calcined to produce Co–Mn/reduced graphene oxide (rGO). This approach not only carbonized the organic framework but also enhanced its electrical conductivity and stability. Our findings demonstrated that the synergistic effects of Co and Mn within the assembled electrode resulted in remarkable performance, achieving a specific capacitance of 3558.65 F g− 1 at 1 A g− 1 and a rate capability of 1000 F g− 1 at 30 A g− 1. The Co–Mn/rGO anode in the asymmetric supercapattery exhibited a broadened operating potential window of 1.5 V, delivering an energy density of 54.65 W h kg− 1 at a power density of 125 W kg− 1, and maintaining 11.375 W h kg− 1 at a high power density of 12,500 W kg− 1. Notably, the capacitance retention rate reached 99.99% after 10,000 cycles at a current density of 10 A g− 1. These results suggest that the developed Co–Mn/rGO composite represents a promising candidate for advanced energy storage systems, offering both high performance and stability.

The accurate detection of vital biomarkers such as Ascorbic Acid (AA), Uric Acid (UA) and Nitrite ( NO2 −) is crucial for human health surveillance. However, existing methods often struggle with concurrent detection and quantification of multiple species, highlighting the need for a more effective solution. To address this challenge, this study aimed to develop a multifunctional electrochemical sensor capable of parallel detection of AA, UA and NO2 − using a synergistic combination of Graphene Oxide (GO) and Cadmium Sulfide (CdS) materials. Notably, the fabricated CdS@GO/Glassy Carbon Electrode (GCE) exhibited exceptional electrochemical activity, as evidenced by Differential Pulse Voltammetry (DPV) analysis. The sensor demonstrated remarkable sensitivity (8.13, 10.12, and 9.05 μA·μM−1·cm−2) and ultra-low detection limits (0.034, 0.062, and 0.084 μM) for AA, UA and NO2 −, respectively. Furthermore, it successfully identified single molecules of each analyte in aqueous and biologic fluid samples, with recovery values comparable to those obtained using High-Performance Liquid Chromatography (HPLC) standard addition methods. The significance of this study lies in developing a novel CdS@ GO/GCE sensor that enables concurrent detection and quantification of multiple vital biomarkers, offering a promising tool for human health monitoring and diagnosis.

Photometric and spectroscopic observations of GV Leo were performed from 2017 to 2024. The light curves show a flat bottom at the primary eclipse and the conventional O’Connell effect. The echelle spectra reveal that the effective temperature and rotation velocity of the more massive secondary are Teff,2 = 5220 ± 120 K and v2 sin i = 223 ± 40 kms−1, respectively. Our binary modeling indicates that the program target is a W-subclass contact binary with a mass ratio of q = 5.48, an inclination angle of i = 81.◦68, a temperature difference of (Teff,1 − Teff,2) = 154 K, and a filling factor of f = 36%. The light asymmetries were reasonably modeled by a dark starspot on the secondary’s photosphere. Including our 26 minimum epochs, 84 times of minimum light were used to investigate the orbital period of the system. We found that the eclipse times of GV Leo have varied by a sinusoid with a period of 14.9 years and a semi-amplitude of 0.0076 days superimposed on a downward parabola. The periodic modulation is interpreted as a light time effect produced by an unseen outer tertiary with a minimum mass of 0.26 M⊙, while the parabolic component is thought to be a combination of mass transfer (secondary to primary) and angular momentum loss driven by magnetic braking. The circumbinary tertiary would have caused the eclipsing pair of GV Leo to evolve into its current short-period contact state by removing angular momentum from the primordial widish binary.

We prepared porous poly(ε-caprolactone)/poly(lactic-co-glycolic acid) (PCL/PLGA) 3D scaffolds with surfaces that were modified through the co-precipitation of calcium phosphate (CAP) with binary drug components, including risedronate (RSD) and hyaluronic acid (HyA). The 3D porous biodegradable PCL/PLGA scaffolds were fabricated by sintering microspheres prepared with a 30/70 PCL/PLGA blend. The co-precipitation of the CAP coating with binary drug components significantly enhanced the proliferation and differentiation of rat mesenchymal stem cells (rMSCs) on the scaffolds. Although the presence of both HyA and RSD positively improved proliferation and differentiation, HyA and RSD were more effective on osteoblastic proliferation and differentiation, respectively. These results strongly demonstrate that the drug effects on osteoblastic responses were closely interconnected. The two drugs affect rMSCs behavior in a concentration-dependent manner, requiring a balance between proliferation and differentiation for optimal bone regeneration. We expect this surface modification technique could potentially be utilized for the fabrication of functionalized biodegradable scaffolds and delivery of drug mixtures.

Abstract Handling imbalanced datasets in binary classification, especially in employment big data, is challenging. Traditional methods like oversampling and undersampling have limitations. This paper integrates TabNet and Generative Adversarial Networks (GANs) to address class imbalance. The generator creates synthetic samples for the minority class, and the discriminator, using TabNet, ensures authenticity. Evaluations on benchmark datasets show significant improvements in accuracy, precision, recall, and F1-score for the minority class, outperforming traditional methods. This integration offers a robust solution for imbalanced datasets in employment big data, leading to fairer and more effective predictive models.