Developed Method Research Articles

Assessment of Children’s Skill in Noticing Unfamiliar Words within Informational Context

ABSTRACT Reading and language standards, such as the Common State Standards for English Language Arts, indicate that primary-grade students should be able to determine the meaning of unfamiliar words from grade-level context. However, to be able to attempt determining the meaning of an unfamiliar word, the word must first be noticed. There is a lack of research on young children developing skill in noticing unfamiliar words, which may be an important skill for their vocabulary development and comprehension. In the present study, theories of word learning, earlier experimenter-created assessments with older learners, and research with multilingual learners guided the development of such an assessment, the Noticing Unfamiliar Words Assessment (NUWA). Instrument development methods, including expert review and piloting, were performed prior to administering the NUWA to a sample of 55 second graders. Results of the exploratory factor analysis supported the hypothesized unidimensional factor structure and indicated that the assessment had an adequate fit and internal consistency reliability. Findings suggest that the NUWA may be helpful in better understanding the developmental nature of skill in noticing unfamiliar words from context while listening and how it contributes to children’s vocabulary knowledge and other literacy skills.

Bayesian inference and optimisation of stochastic dynamical networks

Network inference has been extensively studied in several fields, such as systems biology and social sciences. Learning network topology and internal dynamics is essential to understand mechanisms of complex systems. In particular, sparse topologies and stable dynamics are fundamental features of many real-world continuous-time (CT) networks. Given that usually only a partial set of nodes are able to observe, we consider linear CT systems to depict networks since they can model unmeasured nodes via transfer functions. Additionally, measurements tend to be noisy and with low and varying sampling frequencies. This paper applies dynamical structure functions (DSFs) derived from linear stochastic differential equations (SDEs) to describe networks of measured nodes. A numerical sampling method, preconditioned Crank–Nicolson (pCN), is used to refine coarse-grained trajectories to improve inference accuracy. The proposed method can handle sparsely sampled data and unmeasurable nodes. Monte Carlo simulations indicate that the proposed method outperforms state-of-the-art methods with various network topologies. The developed method can be applied under a wide range of contexts, such as gene regulatory networks, social networks and communication systems.

A fast calculation method for internal blast shock wave in confined structures

Internal explosions are more complicated and destructive than external explosions. It is hard to give accurate predictions of the internal blast load by existing simplified models. In the present work, a fast calculation method for the shock wave of internal blast within a confined space was developed. Firstly, the theoretical bases of this method, the reflection addition rules and the image burst theory, were introduced and validated. Then, the concept of the influence region of image burst for internal blast calculation was proposed, and the detailed calculation method for internal blast load was presented. In addition, the internal blast loads inside a typical cubic structure and a tunnel model were calculated using this developed method, respectively. Moreover, comparison studies between the calculated results and experimental data as well as numerical simulation results were conducted. It was found that the results of the fast calculation model are in good agreement with those of the blast experiment and fine numerical simulation, but the fast calculation takes only less than 2 min. Finally, the advantages and defects of the present method were discussed and further study advices were suggested to improve the accuracy.

Integration and Implementation of Scaled Agile Framework and V-Model in the Healthcare Sector Organization

The development of medical technology devices leads to the introduction and use of agile methods, which enable the delivery of increasingly complex software with the fastest possible innovations. Delivery of the highest quality software must be considered during development, as medical products are important elements in saving human lives. Their development begins with determining a set of product requirements that exactly correspond to it. The development of specified medical products is finally delivered to the customer, who participates in the development. In this article, we present the use and combination of agile methods in software development, which correct and facilitate timely and continuous delivery of products. They also know how to smooth out a quick reaction to the customer’s changing needs and mainly focus on team management and communication. Specific agile methods make it possible to implement development through gradual improvements by integrating customer requirements towards the product. This article identifies three interconnected approaches to integrating agile methods and principles: SCRUM, SAFe, and Kanban combined with the V-model. The methods are gradually analysed based on the literature review, and the article presents a practical application in Siemens Healthcare Slovakia.

Estimating Flow Direction of Circle of Willis Using Dynamic Arterial Spin Labeling Magnetic Resonance Angiography.

The Circle of Willis (COW) is a crucial mechanism for cerebral collateral circulation. This proof-ofconcept study aims to develop and assess an analysis method to characterize the hemodynamics of the arterial segments in COW using arterial spin labeling (ASL) based non-contrast enhanced dynamic magnetic resonance angiography (dMRA). The developed analysis method uses a graph model, bootstrap strategy, and ensemble learning methodologies to determine the time-curve shift from ASL dMRA to estimate the flow direction within the COW. The performance of the method was assessed on 52 subjects, using the flow direction, either antegrade or retrograde, derived from 3D phase contrast (PC) MRI as the reference. A total of 340 arterial segments in COW were evaluated, among which 30 (8.8%) had retrograde flow according to 3D PC. The ASL dMRA-based flow direction estimation has an accuracy, sensitivity, and specificity of 95.47%, 80%, and 96.34%, respectively. Using ASL dMRA and the developed image analysis method to estimate the flow direction in COW is feasible. This study provides a new method to assess the hemodynamics of the COW, which could be useful for the diagnosis and study of cerebrovascular diseases. COW = Circle of Willis; ASL = arterial spin labeling; dMRA =dynamic magnetic resonance angiography; PC = phase contrast.

Effects of strain rate and bedding on shale fracture mechanisms

Anisotropic shale is commonly distributed in nature and undergoes both dynamic compressive and tensile load in geological engineering. In this article, the coupling effects of strain rate and bedding structures on the fracture mechanisms of shale are investigated using a split Hopkinson pressure bar (SHPB) system. The tension–compression comparative study is conducted. The Brazilian disc method is modified, and a unified strain rate measurement method is established to comprehensively analyze shale’s fracture-related properties, including moduli, strength and energy dissipation. The results indicate that the energy dissipated by shale fracture is strongly related to its strength. The strain rate and bedding affect the anisotropic strength and dissipated energy by changing the crack density and crack propagation mode. At low or medium strain rates, the bedding orientation of shale determines the direction of crack propagation. However, at high strain rates, microcracks in different directions are widely activated in the shale, which increases the fracture degree and decreases the difference in fracture toughness between the shale’s matrix and bedding planes. This also leads to a decrease in anisotropy and causes the bedding planes to lose control over the direction of crack propagation. Additionally, the dynamic tensile strength of shale increases faster than the compressive one with the strain rate, leading to a reduction in the proportion of tensile failure under impact load. Based on the experimental findings, a new anisotropic damage constitutive model is developed to characterize the dynamic properties of shale. This model reflects the anisotropic damage evolution behaviors and aligns well with experimental results, offering a theoretical foundation for predicting shale’s dynamic fracture behavior. In addition to shale, the developed experimental methods and theoretical models can also be applied to the fracture analysis of other brittle transversely isotropic materials.

A preliminary study on generation of axle box vibration spectra for metro vehicles in China using the developed spectral synthesis method

ABSTRACT In-service metro vehicles in China exhibit diverse vibration behaviours. This study focuses on analysing the vibration characteristics of these vehicles, with a particular focus on the complex non-stationary axle box vibrations measured on-site, as they closely correlate with wheel-rail interaction dynamics. A developed PSD-based synthesis method is first proposed to quantify the axle box vibration. The axle box vibration spectra of the metro vehicles in China are then generated from the measurements for various vehicles. Furthermore, a multi-line segment flat spectrum, designed specifically for the shaker loading, has been produced. The results show that the proposed method can determine the parameters (segment number, quantile) by the energy conservation law, which avoids the empirical value selection in previous methods and can be effectively utilized to generate the vibration spectra of the axle box. In addition, a notable distinction exists between the IEC 61,373 standard spectrum and the axle box vibration spectra of Chinese metro vehicles. While the former represents a single-peak spectrum ranging from 20 ~ 100 Hz, the latter consists of a multi-peak spectrum with peak frequency bands exceeding 100 Hz. The wear condition of the wheel-rail surface has a pronounced contribution to the vibration spectral magnitude. In cases of wheel-rail surface severe wear, the vibration spectrum values in the Z direction surpass the standard spectrum values across most frequencies, with the highest spectral value notably observed within 50 ~ 200 Hz. Such observations should be thoroughly taken into account in the analysis of fatigue damage to mounted equipment.

Pixel-Level Crack Identification for Bridge Concrete Structures Using Unmanned Aerial Vehicle Photography and Deep Learning

Traditional manual inspection technology has the problems of high risk, low efficiency, and being time-consuming in bridge safety management. The unmanned aerial vehicle (UAV)-based detection technology is widely used in bridge structure safety monitoring. However, the existing deep learning-based concrete crack identification method has great limitations in dealing with complex background and tiny cracks in bridge structures. To address these problems, this study designs a crack pixel-level high-performance segmentation model for bridge concrete cracks that is suitable for UAV detection scenarios using machine vision (MV) and deep learning (DL) algorithms. First, considering the high requirements for the computing performance of the MV-based model for UAV-based detection, the ResNet-18-based lightweight convolutional neural network is used to represent the traditional large-scale backbone network of the pyramid scene parsing network (PSPNet) to develop a high-performance crack automatic identification model. Then, considering that bridge concrete cracks have the characteristics of subtle shapes and complex backgrounds, the spatial position self-attention module is inserted into the PSPNet to improve its detection accuracy. A concrete bridge is used for the case study, and a dataset of cracks in bridge concrete structures collected by UAVs is constructed and used for model training. The experimental results show that the loss function of the developed method in the training process results in a smooth decline, and the developed algorithm achieves the evaluation indicators of 0.9008 precision, 0.8750 recall, 0.8820 accuracy, and 0.9012 IOU on the bridge concrete crack dataset, which are significantly higher than other state-of-the-art baseline methods. In addition, four common UAV bridge detection scenarios, including low light, complex crack forms, high background roughness, and complex background scenes, are used to further test the crack detection ability of the developed crack identification model. The experimental results show that the proposed crack identification method can effectively overcome interference and real-size pixel-level segmentation of crack morphology. In addition, it also achieved a detection efficiency of 35.04 FPS, which shows that the real-time detection ability of the method has good applicability in the UAV detection scene.

A Novel Flow Injection Method with Chemiluminescence Detection for the Determination of Carmoisine in Gelatin Desserts.

Carmoisine dye, a red azo food colorant commonly utilized to impart a red color to synthetic food products, is the subject of this study. Here, we present a novel reversed flow injection analysis with a chemiluminescence detection (FIA-CL) method employing a newly developed homemade flow cell to determine carmoisine dye. This developed method is based on the inhibition effect of the dye on the chemiluminescence light (CL) emission generated from a luminal-hypochlorite system, whereby the reduction in CL intensity correlates directly with the concentration of carmoisine dye. Investigations into various analytical parameters were conducted to enhance method efficiency and applicability. A linear calibration graph of 4.0 to 100.0µg mL-1 was established (R² = 0.9993), with a detection limit of LOD = 2.93µg mL-1. Subsequent application of the proposed method to analyze gelatine dessert samples yielded results in reasonable agreement with those obtained using the reported HPLC method, as evidenced by student t-test and F-test analyses.

Development and validation of high-performance liquid chromatography method for the simultaneous quantification of rivastigmine hydrogen tartrate and asiaticoside co-loaded in niosomes: A Box–Behnken design approach

Rivastigmine hydrogen tartrate (RHT), a reversible cholinesterase inhibitor, is considered as the first-line therapy for mild to moderate Alzheimer’s disease. Asiaticoside (AS), a pentacyclic triterpenoid saponin, is well known as cognitive enhancer due to its antioxidant effect. Based on the hypothesis of their synergistic therapeutic potential, RHT and AS were co-encapsulated in niosomal formulation. A simple, precise, and accurate high-performance liquid chromatography method was developed for simultaneous quantitative analysis. The chromatographic parameters were optimized by Box-Behnken experimental design. The separation was performed on a reversed-phase Phenomenex C18 (150 mm × 4.6 mm, 5 μm) column at 30 °C under the UV detection of 210 nm. The optimized mobile phase consisted of a mixture of 20 mM potassium dihydrogen phosphate buffer (pH 2.6) and acetonitrile (72:28 % v/v) under the isocratic mode at the flow rate of 0.9 mL/min. The developed method was fully validated under the ICH guidelines and could be successfully applied for simultaneous quantitative analysis of RHT and AS in niosomal formulation.

Study of the dielectric properties of decomposing materials during heating

The dielectric properties of materials when heated at ultrahigh frequencies are determined by methods based on the use of waveguide cavity resonators. However, as the sample is in the closed volume during measurements, the dielectric properties of destructive material thus determined can contain errors attributed filling of the resonator volume and depositing products of the sample decomposition on the internal conducting surfaces of the resonator. We present the results of studying the dielectric properties of destructive materials under heating in waveguide resonator cavity using methods that exclude the impact of destruction products on the accuracy of measurement. To reduce the influence of destructive vapors rising from the surface of the sample during the destruction process, additional thin quartz plates, placed on the sample surface were used. In addition, cuvettes with a lid were also used to prevent destruction products from entering the resonator volume. A method for taking into account the effect of plates and cuvettes in calculating the dielectric constant and dielectric loss tangent of a material sample when heated in a cavity resonator is presented. Changes in the dielectric properties of a composite material consisting of quartz fabric impregnated with an aluminochromophosphate binder and fiberglass laminate made of quartz fabric impregnated with a phenol-formaldehyde binder were studied in a temperature range above the onset of the destruction (up to temperatures at which loss of the structural strength of the material is observed). The developed method can be used in studying the thermophysical properties of destructive materials to assess changes in their dielectric characteristics under operation conditions of high-temperature heating.

Enhancing instrumental sustainability: a spectrophotometric approach for quantifying iodate in nutritionally significant salts through the adaptation of the classical iodometry method

With the primary objective of transitioning from the classical iodometric method to an instrumental approach, preserving precision in the analytical process, this study presents the development of a spectrophotometric method for quantifying iodate (iodine equivalent) in nutritionally significant salts. Therefore, the wavelengths of maximum absorption of the I3- (350.8 nm) and I3-starch (574.1 nm) complexes were determined. The complexes demonstrated stability, as indicated by the respective molar extinction coefficient values (ε) obtained over the course of the reaction time (measured up to 1h, at 5-minute intervals, and at 28°C), for concentrations ranging from 7.81×10-6 to 1.25×10-4 mols/L of KIO3. A linear correlation between concentration and absorbance was established within the specified concentration range, with detection and quantification limits defined as 0.09 and 0.29 mg/L (350.8 nm) and 0.13 and 0.39 mg/L (574.1 nm), respectively, and calculated recovery of 102.9%. The reproducibility of the method was assessed by varying the analyst, the equipment, and the analysis day. Using the developed method, it became feasible to quantify the elemental iodine equivalent in three commercially available salts commonly employed for food purposes. When compared to the classical/traditional iodometric method, the spectrophotometric method for quantifying iodate in salts showcases sustainability with minimized solvent and reagent usage, reduced waste generation, shorter analysis time, and lower sample requirements, aligning with environmentally conscious analytical practices.

Development of HPLC method for isomeric impurities of key starting material of novel oral anticoagulant drug; Edoxaban Tosylate Monohydrate

Reverse-phase high-performance liquid chromatography method has been developed for the determination of EDO-S1 stereoisomeric impurities such as isomer 1, isomer 2, isomer 3, isomer 4, isomer 5, isomer 6 and isomer 7 with good resolution using the column, Bakerbond C18 (150 × 4.6 mm; 3 μm). The separation was achieved with mobile phase-A (10 mM dipotassium hydrogen phosphate pH-7.0 with 10% orthophosphoric acid solution in Milli-Q water) and mobile phase-B (n-Propanol: Acetonitrile ratio of 20:30 % V/V), which consisted of mobile phase mixture in the combination of moilephase-A: mobile phase-B (85:15). The total run time was 30 min at 0.8 mL/min flow rate, 20 µL injection volume and 30 ℃ column oven temperature. The column eluate was monitored at 210 nm to quantify the impurities The method showed adequate specificity, sensitivity, linearity, accuracy, precision, and robustness inline to ICH tripartite guidelines. The limit of detection and quantification limits were 0.1 and 0.3 μg mL−1, respectively, for all isomeric impurities and EDO-S1. The developed method was found to be linear over the concentration range of LOQ to 150% of specification range for isomeric impurities with a correlation coefficient >0.999. The method was precise (%RSD < 5.0), robust, and accurate (with 85%–115% recovery).

A simple and sensitive ultra-high performance liquid chromatography tandem mass spectrometry method for the quantitative analysis of VX-548 in monkey plasma: Method validation and application to pharmacokinetic study.

VX-548 is an orally active and highly selective NaV 1.8 inhibitor that is undergoing development for the treatment of acute pain. The aim of this study was to develop a liquid chromatography-tandem mass spectrometric (LC-MS/MS) method for the measurement of VX-548 in monkey plasma. VX-548 was extracted from the plasma using acetonitrile-mediated protein precipitation. The quantitative analysis was performed on a Thermo Vantage TSQ mass spectrometer with ibrutinib as an internal standard. Chromatography was performed on a Waters ACQUITY UPLC BEH C18 column with 0.1% aqueous formic acid and acetonitrile as mobile phase. The precursor-to-product ion transitions were m/z 474.2 > 165.0 and m/z 441.2 > 138.1 for VX-548 and internal standard, respectively. This developed method was successfully validated in the concentration range of 1-1000 ng/mL. The calibration curve showed excellent linearity with a correlation coefficient of >0.999. The precision expressed as relative standard deviation (RSD) was <8.4%, whereas the accuracy denoted as relative error (RE) ranged from -5.0% to 9.1%. The mean recovery was >84%. VX-548 was stable in monkey plasma after storage under certain conditions. The validated method was successfully applied to the pharmacokinetic study of VX-548 in monkey plasma after single oral (2 mg/kg) and intravenous (1 mg/kg) administrations.

Rapid determination of 111 anti-infective drugs possibly added in cosmetics using high-performance liquid chromatography-tandem mass spectrometry with scheduled multiple reaction monitoring.

Illegal addition of anti-infective drugs to cosmetics at low concentrations has been found. The illicit addition of anti-infective drugs encompasses a wide variety of medications. The current sample purification methods are inadequate to detect all these compounds. A sensitive, wide-coverage, and weak-matrix-effect measurement method needs to be established to address this issue. Samples were extracted using acetonitrile, diluted 25 times, and then analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to detect 111 anti-infective drugs. The method was validated and assessed for matrix effect before being applied to cosmetic products. The calibration curves for the analytes exhibited a strong correlation coefficient (r > 0.995). The limit of detection ranged from 0.006 to 0.6mg/kg. Matrix effects were significantly improved after a 25-fold dilution. The method was successfully applied to various cosmetics. Two of 82 samples tested contained lincomycin and miconazole, respectively. The developed method is quick and reliable to analyze anti-infective drugs in cosmetics, with potential for both qualitative and quantitative analyses. It is a valuable tool for cosmetic research and development, contributing to safer and more effective cosmetic products.

Solvent Bar Microextraction Method Based on a Natural Deep Eutectic Solvent and Multivariate Optimization for Determination of Steroid Hormones in Urine and Water

Steroid hormones may pose potential risks to both human health and wildlife, primarily through the consumption of medication or polluted food and water; efforts are being made to monitor their levels in the human body and regulate and minimize their releases to the environment. In this study, a simple and environmentally friendly sample preparation method was developed to simultaneously determine three steroid hormones in urine and water samples. A monoterpene (menthol) and a fatty acid (lauric acid) were combined in various ratios to form a hydrophobic deep eutectic (HDE) solvent as an extraction solvent in solvent bar microextraction (SBME). Using a univariate strategy, a menthol-to-lauric acid HDE ratio of 4:1 and a pH 7 of the sample solution resulted in the highest extraction efficiency (EE%) of the selected steroids. The computational methods have been employed to predict a 4:1 HDE interaction with chosen steroids. Additionally, chemometric approaches suggested that the optimal extraction conditions involved HDEs as extract solvent confined within three SBME devices directly immersed into a 20 mL sample solution with a 30 min extraction time, followed by ultrasonication within 200 μL of elution solvent for a 5 min elution time. Under optimized conditions, the method calibration graph for the spiked selected steroids in the water and urine samples showed good linearity with R2 ≥ 0.994 with limits of detection/quantification lower than 0.40/1.35 μg L−1 and repeatability/reproducibility (RSD%, n = 5) lower than 5.09/7.11. The developed method allows a safe, rapid, and reliable analysis of three steroid hormones in human urine and water samples without using toxic volatile organic solvents.

Priority Competencies for Designated Education Officers in the Veterans Health Administration.

Designated Education Officers (DEOs) at Veteran Health Administration (VHA) hospitals are senior educational leaders tasked with oversight of all clinical training at a particular facility. They prioritize dozens of tasks and responsibilities each day, from educational policy and strategy to staff management, financial planning, onboarding of trainees, and facility planning and management. Clarifying priority competencies for the role can help executives recruit, appoint, and evaluate capable personnel and promote effective, efficient performance. Using a federally developed method of competency analysis, researchers consulted a panel of subject-matter experts to identify priority competencies for DEOs, using data from a 2013 study that operationalizes competencies for more than 200 federal jobs. The research identified 25 primary competencies within 6 leadership domains. Five of the primary competencies cut across all leadership domains. Veteran Health Administration subject-matter experts in educational leadership say the identified competencies are urgently needed, critical for effective leadership, and valuable for distinguishing superior DEO performance. The competencies are relevant to VHA and perhaps other senior academic leaders who develop health professions education programs, oversee clinical training, and manage educational change. In military training facilities, attending to these competencies can help Designated Institutional Officials responsible for graduate medical education become more credible partners to other hospital leaders and contribute to becoming a high reliability organization. Executives identifying, recruiting, and appointing VHA DEOs and Designated Institutional Officials at military training facilities should consider these competencies when assessing candidates.

Au@Cu2O composite nanorods as mimetic peroxidase for glucose colorimetric sensing

Au@Cu2O composite nanorods (CNRs) were prepared by depositing Cu2O onto the surface of Au nanorods. The as-prepared Au@Cu2O CNRs exhibit enhanced peroxidase-like activity compared to bare Au NRs and Cu2O, and can effectively catalyze the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) in the presence of H2O2, resulting in the formation of a blue-colored oxidized TMB (ox-TMB). Thus, the feasibility of Au@Cu2O CNRs as a peroxidase mimic for H2O2 sensing was investigated through colorimetric method. Combined with glucose oxidase, a simple colorimetric method for glucose detection was further established, which exhibits good analytical performance, including a wide linear response range of 0.5 ∼ 140 µM, a low detection limit of 0.18 µM, and good selectivity. Most of all, the achieved satisfactory recovery (96.0 % ∼ 110.8 %) and precision (relative standard deviation less than 2.8 %) in serum samples confirm the potential of the developed method for practical applications.

Fullstack Implementation Using Angular Framework and Springboot in the Participant Admission Information System New Education (PPDB) (RA DAARUN Case Study - NISAA)

RA DAARUN-NISAA is an educational institution situated in a specific area in Bandung. It annually admits new students. This university attracts many fans due to its engaging learning system that prevents students from feeling bored and its rigorous teaching system. Nevertheless, the admission process at RA DAARUN-NISA is still carried out manually. Due to the absence of a student admission data management system, the incoming data has yet to be integrated into the system. Since the student admission process has yet to be digitized, there are still instances of duplicate data. Consequently, the operator must manually organize the data before inputting it into the central system. A design for an information system to accommodate new students was developed based on the issues identified in the research findings. The research methodology employed in this thesis is the descriptive analysis method. The system development method employed is the SDLC (Software et al.) method, which utilizes a waterfall model. The system was designed utilizing the Angular framework for the front end and Springboot for the back end, with PostgreSQL as the database. The research concludes that an information system has been developed at RA DAARUN-NISAA to facilitate the registration process for new students. This system allows prospective students to register more efficiently while enabling direct management and minimization of data during the registration process. the duplicate. Recommendations for researching student admission information systems involve creating and maintaining a system that operates efficiently and at its highest potential.

A hybrid deep reinforcement learning approach for a proactive transshipment of fresh food in the online–offline channel system

To reduce the waste of fresh foods, one of the e-commerce companies in South Korea utilizes lateral transshipment in the network of online platforms and offline shops, which is called the online–offline channel system (OOCS). Even though the OOCS has achieved success in real practice, there is room for further study on this system with regard to deriving a transshipment policy. For this reason, this study aims to develop a solution approach that could derive a promising policy and analyze the impacts of transshipment in the OOCS. The main contributions are summarized as follows. First, we propose a model to deal with the proactive transshipment of perishable products in the OOCS. In particular, this is the first study that introduces the concept of the heterogeneous shelf life considering different properties of online and offline channels. Second, we develop the hybrid deep reinforcement learning (DRL) approach by combining the soft actor–critic algorithm with two novel acceleration methods. The developed method could obtain a promising policy without assumptions about demand distribution and mitigate computational burdens by reducing action spaces. On a set of experiments carried out on real-world demand data, the transshipment policy derived from the hybrid DRL approach could obtain the best profit compared to existing algorithms. Third, we examine the impacts of transshipment by differing types of demand and varying the unit transshipment cost parameter. We find that transshipment substantially reduces the outdating cost by allowing the offline channel to make good use of the old products that will be discarded in the online channel, which is new to the literature.