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Radiation exposure can immediately trigger a burst of reactive oxygen species (ROS), which can induce severe cell death and long-term tissue damage. Therefore, instantaneous release of sufficient radioprotective drugs is vital to neutralize those accumulated ROS in IR-exposed areas. To achieve this goal, we designed, synthesized, and evaluated a novel oral ROS-responsive radioprotective compound (M1) with high biocompatibility and efficient ROS-scavenging ability to act as a promising oral drug for radiation protection. The compound is stably present in acidic environments and is hydrolyzed in the intestine to form active molecules rich in thiols. M1 can significantly remove cellular ROS and reduce DNA damage induced by γ-ray radiation. An in vivo experiment showed that oral administration of M1 effectively alleviates acute radiation-induced intestinal injury. Immunohistochemical staining showed that M1 improved cell proliferation, reduced cell apoptosis, and enhanced the epithelial integrity of intestinal crypts. This study provides a promising oral ROS-sensitive agent for acute intestinal radiation syndrome.

Intelligent power grid fault diagnosis is of great significance for speeding up fault processing and improving fault diagnosis efficiency. However, most of the current fault diagnosis methods focus on rule diagnosis, relying on expert experience and logical rules to build a diagnosis model, and lack the ability to automatically extract fault knowledge. For switch refusal events, it is difficult to determine a refusal switch without network topology. In order to realize the non-operating switch identification without network topology, this paper proposes a power grid fault diagnosis method based on deep reinforcement learning for alarm information text. Taking the single alarm information of the non-switch refusal sample as the research object, through the self-learning ability of deep reinforcement learning, it learns the topology connection relationship and action logic relationship between equipment, protection and circuit breakers contained in the alarm information, and realizes the detection of fault events. The correct prediction of the fault removal process after the occurrence, based on this, determines the refusal switch when the switch refuses to operate during the fault removal process. The calculation example results show that the proposed method can effectively diagnose the refusal switch of the switch refusal event, which is feasible and effective.

The propulsion systems of hybrid electric ship output and load demand have substantial volatility and uncertainty, so a hierarchical collaborative control energy management scheme of the ship propulsion system is proposed in this paper. In a layer of control scheme, the traditional perturbation algorithm is improved. Increasing the oscillation detection mechanism and establishing the dynamic disturbance step length realizes the real-time stability of maximum power point tracking control. In the second-layer control scheme, the power sensitivity factor and voltage and current double closed-loop controller is introduced. By designing a two-layer coordinated control strategy based on the dynamic droop coefficient, the problem of voltage and frequency deviation caused by load switching is solved. In the third-layer control scheme, due to the need of the optimal scheduling function, the multi-objective particle swarm optimization algorithm was improved through three aspects: introducing the mutation factor, improving the speed formula, and re-initializing the strategy. Compared with other algorithms, this algorithm proves its validity in day-ahead optimal scheduling strategy. The superiority of the hierarchical collaborative optimization control schemes proposed was verified, in which power loss was reduced by 39.3%, the overall tracking time was prolonged by 15.4%, and the environmental cost of the diesel generator was reduced by 8.4%. The control strategy solves the problems of the steady-state oscillation stage and deviation from the tracking direction, which can effectively suppress voltage and frequency fluctuations.

Construction machinery is necessary in postdisaster emergency rescue missions involving the destruction of ruins. However, their mechanical vibrations can interfere with the detection of human survivors using ultrawideband (UWB) radar. Traditional methods detect and identify humans by determining maximum energy and checking respiratory frequency. However, they lose effectiveness because mechanical vibration is associated with a frequency band which is similar to the human respiration band, but it has higher energy. This study proposes a novel method to distinguish human vibrations from mechanical vibrations. After preprocessing, wavelet entropy decomposition was implemented on the radar data. An improved, censored mean-level detector, constant false-alarm rate algorithm was utilized to automatically identify the position of human and mechanical vibrations. A novel feature is then extracted by calculating the half-height width of the target’s wavelet entropy. Finally, the results of two independent sample t-tests prove that there is a significant statistical difference between the feature values of humans and the mechanical vibrations (p < 1.9 × 10−6), thus proving the effectiveness of the method. We envisage that the proposed method can be used in postdisaster rescue missions to improve the accuracy and speed of identifying human targets. Therefore, more survivors may be rescued.

Based on the daily minimum air temperature (Tmin) data and the daily precipitation data from the NCC/CMA combined with the NCEP/DOE reanalysis data, the intraseasonal features and circulations of the winter cold precipitation events (CPEs) in southern China under the influence of strong Madden–Julian oscillation (MJO) were explored. The results show that: (1) Winter temperatures in southern China are characterized by intraseasonal oscillations (ISOs) of 10–30-d and 30–60-d, with six CPEs under strong MJO all occurring during these two intraseasonal scales in cooling phases. The invasion of cold air coupled with the availability of appropriate moisture conditions in southern China is more conducive to the CPEs. (2) A cyclone and anticyclone lying to the east of the Ural Mountains and the northwest of Lake Baikal at 925-hPa gradually move southeastward. The merging of the low-frequency (LF) blocking highs over the Ural Mountains and the North Pacific Ocean at 500-hPa leads to the contraction and southward movement of the LF cold vortex. The following anomalous northerly winds steer the cold air towards southern China. The cold advection is the dominant term in the cooling process, while the adiabatic cooling accompanied with ascending motion is also beneficial to the cooling process. (3) MJO has some effect on the LF blocking highs and the cold vortex in the mid-high latitudes and induces the CPEs over southern China. The joint effect of mid-high and low latitudes on the 30–60-d intraseasonal oscillation scale can have a significant impact on the cooling and precipitation processes of CPEs.

Obesity is a prevalent chronic disease worldwide. In this study, we screened lactic acid bacteria (LAB) suitable for fermenting Shenheling extract (SHLE) to enhance its anti-obesity efficacy and improve flavor. Using SHLE as the medium, a single strain was inoculated and the lactic acid bacteria suitable for growth in SHLE were preliminarily screened through a growth curve. The growth of the initially screened LAB was characterized in detail by the pH value, titration acidity and viable bacteria count. At the same time, appropriate LAB were selected with the lipase activity inhibition rate, α-glucosidase activity inhibition rate and a sensory evaluation as the response indicators. As a result, 6 of the 12 strains of lactic acid bacteria grew well in SHLE. The fermentation of five representative LAB could significantly improve the inhibition rate of the lipase activity of SHLE and maintain the inhibition rate of the α-glucosidase activity at a high level. In addition, fermentation removed the original flavors of SHLE such as grass, bitterness and cassia and added a sour taste, fruity aroma and cool taste. Among them, Lactobacillus fermentum grx08 and Lactobacillus rhamnosus hsryfm1301 gave SHLE a soft sour taste after fermentation. L. fermentum grx08, L. rhamnosus grx10 and hsryfm1301 imparted a moderately fruity aroma to SHLE after fermentation. In summary, L. fermentum grx08 and L. rhamnosus hsryfm1301 were the candidate strains for fermenting SHLE to produce good-flavored slimming functional drinks.

To achieve the highest possible integration storage density in the V-point structure, the working current of the selector in the one-selection one-resistance (1S1R) structure should match with the resistance random access memory (RRAM). In this study, a selector device is designed with a Ti/NbOx/Ti/Pt structure through the magnetron sputtering method and achieves excellent performance of threshold switching under ultra-large compliance current (CC) up to 100 mA. Furthermore, both the switching voltages and the OFF-state resistance of the device demonstrate excellent stability even when CC is increased to a milliampere level, attributed from the existence of metallic NbO in the switching layer. This study provides evidence that a Ti/NbOx/Ti/Pt device has a great potential to drive RRAM in the V-point structure.

In the Southeast Margin of the Tibetan Plateau, low-velocity sedimentary layers that would significantly affect the accuracy of the H-κ stacking of receiver functions are widely distributed. In this study, we use teleseismic waveform data of 475 events from 97 temporary broadband seismometers deployed by ChinArray Phase I to obtain crustal thicknesses and Poisson’s ratios within the Chuxiong-Simao Basin and adjacent area, employing an improved method in which the receiver functions are processed through a resonance-removal filter, and the H-κ stacking is time-corrected. Results show that the crustal thickness ranges from 30 to 55 km in the study area, reaching its thickest value in the northwest and thinning toward southwest, southeast and northeast. The apparent variation of crustal thickness around the Red River Fault supports the view of southeastern escape of the Tibetan Plateau. Relatively thin crustal thickness in the zone between Chuxiong City and the Red River Fault indicates possible uplift of mantle in this area. The positive correlation between crustal thickness and Poisson’s ratio is likely to be related to lower crust thickening. Comparison of results obtained from different methods shows that the improved method used in our study can effectively remove the reverberation effect of sedimentary layers.

In the age of the Internet of Things, online data have witnessed a significant growth in terms of volume and diversity, and research into information retrieval has become one of the important research themes in the Internet-oriented data science research. This paper introduces a novel domain knowledge centric methodology aimed at improving the accuracy of using machine learning methods for relation extraction from text data, which is critical to the accuracy and efficiency of information retrieval-based applications, including recommender systems and sentiment analysis. The proposed methodology makes a significant contribution to the processes of domain knowledge-based relation extraction including interrogating Linked Open Datasets to generate the relation classification training data, addressing the imbalanced classification in the training datasets, determining the probability threshold of the best learning algorithm, and establishing the optimum parameters for genetic algorithms, which were utilized to optimize the feature selection for the learning algorithms. The experimental evaluation of the proposed methodology reveals that the adopted machine-learning algorithms exhibit higher precision and recall in relation extraction in the reduced feature space optimized by our implementation. The considered machine learning includes support vector machine, perceptron algorithm uneven margin, and K-nearest neighbors. The outcome is verified by comparing against the random mutation hill-climbing optimization algorithm using Wilcoxon signed-rank statistical analysis.

Electric vehicle (EV) has been popularized and promoted on a large scale because of its clean and efficient features. Charging this increasing number of EVs is expected to have an impact on the electricity grid and traffic network. Therefore, it is necessary to model and forecast the EV charging demand. Most of the existing researches have not utilized real-world traffic data to analyze the EV charging demand. Few researches have considered and analyzed the characteristics of space-time transfer of charging load in urban functional areas. As an emerging mode of transportation, however, online ride-hailing trip data provide an ideal source for analyzing traffic planning and operation. On the basis of this, a charging demand forecasting model of EVs based on a data-driven approach was presented in this paper. In this methodology, it is firstly assumed that residents' transportation trip demand is not restricted by vehicle categories(electric or fuel vehicles). The original trip trajectory data of Didi online ride-hailing were conducted to model via data mining and fusion. And the process of data analysis included region-scope selection, spatial grid modeling, trajectory data mapping, retrieval data identification and urban functional area clustering as well as traffic network modeling. Through modeling and processing, the following regenerative feature data were obtained: functional regional division (i.e., residential areas, industrial areas, commercial areas, and public service areas), trip rule distribution (i.e., temporal distribution and spatial distribution on weekdays, weekends and holidays) and actual driving path (i.e., driving path with the shortest distance or with the minimum time-consuming). And then, considering the movable load feature of EVs, vehicles were subdivided into three kinds such as private vehicles, taxis and other public vehicles, and the single EV model with driving and charging characteristic parameters was established. Furthermore, the regeneration data obtained from modeling and analysis along with the determined single EV model were supported as data sources and model for the charging demand forecast architecture. At last, the actual urban traffic network in Nanjing, China was selected as an example to design the path planning experiments and charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to realistically simulate the actual dynamic driving process of EVs, and effectively predict the spatial-temporal distribution characteristics and load transfer trends of charging demands in different date type as well as different functional areas. The model also lays a theoretical foundation for the subsequent research on investment and construction of charging facilities, as well as charging control and charging guidance of EVs.

Bipolar membranes, a new type of composite ion exchange membrane, contain an anion exchange layer, a cation exchange layer and an interface layer. The interface layer or junction is the connection between the anion and cation exchange layers. Water is dissociated into protons and hydroxide ions at the junction, which provides solutions to many challenges in the chemical, environmental and energy fields. By combining bipolar membranes with electrodialysis technology, acids and bases could be produced with low cost and high efficiency. The interface layer or junction of bipolar membranes (BPMs) is the connection between the anion and cation exchange layers, which the membrane and interface layer modification are vital for improving the performance of BPMs. This paper reviews the effect of modification of a bipolar membrane interface layer on water dissociation efficiency and voltage across the membrane, which divides into three aspects: organic materials, inorganic materials and newly designed materials with multiple components. The structure of the interface layer is also introduced on the performance of bipolar membranes. In addition, the remainder of this review discusses the challenges and opportunities for the development of more efficient, sustainable and practical bipolar membranes.

Geopolymer is a porous inorganic material with a three-dimensional mesh structure, good mechanical properties, a simple preparation process (no sintering) and a low economic cost, and it is environmentally friendly. Geopolymer concrete has been widely used in the construction field, and many other studies have revealed that geopolymer will become one of the most promising inorganic materials with unique structure and properties. This paper provides a review of the development and current status of geopolymers and briefly explains the effects of material proportioning, experimental factors and activators on geopolymer performance. Because of the advantages of high specific surface area and high porosity, geopolymers could be used as adsorbent materials. This paper summarizes the research progresses of the adsorption of metal cations, anions, dyes, and gases by geopolymers, which emphasizes the geopolymer membranes in adsorption, and discusses the challenges and opportunities for the development of more efficient, sustainable and practical adsorption protocols.

Although direct damage to nuclear DNA is considered as the major contributing event that leads to radiation-induced effects, accumulating evidence in the past two decades has shown that non-target events, in which cells are not directly irradiated but receive signals from the irradiated cells, or cells irradiated at extranuclear targets, may also contribute to the biological consequences of exposure to ionizing radiation. With a beam diameter at the micrometer or sub-micrometer level, microbeams can precisely deliver radiation, without damaging the surrounding area, or deposit the radiation energy at specific sub-cellular locations within a cell. Such unique features cannot be achieved by other kinds of radiation settings, hence making a microbeam irradiator useful in studies of a radiation-induced bystander effect (RIBE) and cytoplasmic irradiation. Here, studies on RIBE and different responses to cytoplasmic irradiation using microbeams are summarized. Possible mechanisms related to the bystander effect, which include gap-junction intercellular communications and soluble signal molecules as well as factors involved in cytoplasmic irradiation-induced events, are also discussed.

Obesity is a common global problem. There are many fat-reducing herbal prescriptions in traditional Chinese medicine that have been proven to be safe and functional during long-term application. Microbial fermentation can improve the efficacy of herbal medicine and improve the unsavory flavor. In this study, Shenheling extract (SHLE) composed of six medicine food homology materials was used as the research object. The purpose of this study was to evaluate the effects of Lactobacillusfermentum grx08 fermentation on the antiobesity efficacy and flavor of SHLE. We found that L. fermentum grx08 grew well in SHLE. After 72 h of fermentation, the total polysaccharides, total flavonoids, total polyphenols and total saponins of SHLE decreased, but the lipase inhibitory activity and total antioxidant capacity (FRAP) were significantly increased (p < 0.01). There were no significant differences in the α-glucosidase inhibition rate and DPPH· clearance rate before or after fermentation (p > 0.05). In addition, the fermentation reduces the unpleasant flavors of SHLE such as bitterness and grassy and cassia flavors. This study demonstrates that SHLE fermented by L. fermentum grx08 improved some anti-obesity functions and improved the unpleasant flavor.

NAD+, a co-enzyme involved in a great deal of biochemical reactions, has been found to be a network node of diverse biological processes. In mammalian cells, NAD+ is synthetized, predominantly through NMN, to replenish the consumption by NADase participating in physiologic processes including DNA repair, metabolism, and cell death. Correspondingly, aberrant NAD+ metabolism is observed in many diseases. In this review, we discuss how the homeostasis of NAD+ is maintained in healthy condition and provide several age-related pathological examples related with NAD+ unbalance. The sirtuins family, whose functions are NAD-dependent, is also reviewed. Administration of NMN surprisingly demonstrated amelioration of the pathological conditions in some age-related disease mouse models. Further clinical trials have been launched to investigate the safety and benefits of NMN. The NAD+ production and consumption pathways including NMN are essential for more precise understanding and therapy of age-related pathological processes such as diabetes, ischemia–reperfusion injury, heart failure, Alzheimer’s disease, and retinal degeneration.

The accurate modeling of the charging characteristics of electric vehicles (EVs) is the basis for the load forecasting, infrastructure planning, and orderly charging management. While, research based on the measured charging data of EVs is seldom carried out, and the concrete modeling of the correlations of various parameters is a gap in the knowledge. Aiming at this, we carried out an investigation based on operational data, from August 2016 to August 2017, of an EV charging service company in Nanjing, China. The time-energy characteristics of EV charging behavior can be described using the probability distributions and correlations of three charging parameters, i.e., charging start time, charging duration, and charged capacity. In this paper, we fitted the probability densities of these charging parameters using the kernel estimation method and verified the correlations of time parameters of the charging behavior. Multiple copula functions were used to model the correlation between the time and energy parameters of different types of charging behaviors. On this basis, we also carried out stochastic simulation for the load curve of disordered charging and analyzed the potential of the EV charging load participating in the orderly management and its coordination with the output of power generation using renewable energy.

Bidirectional wireless power transmission (BD-WPT) systems are applicable to occasions that need bidirectional energy flows, such as vehicle-to-grid technology. The traditional BD-WPT systems work at a fixed frequency and the optimal transmission efficiency of the systems is fixed. Combining the frequency characteristics of AC resistance, the mathematical model of variable frequency BD-WPT system is built in this paper. Then, the characteristics of transmission efficiency and the characteristics of maximum active power are analyzed based on the model. In order to make the BD-WPT system have higher transmission efficiency, a BD-WPT system with variable structural parameters and the corresponding segmented powerefficiency coordinated control strategy are proposed in this paper. According to different reference power on the secondary side, the BD-WPT system can work in different resonant frequency segments by controlling the switching of corresponding relays in the capacitor arrays on the primary and secondary sides and changing working frequency of the H-bridge converters on the primary and secondary sides. By using the proposed control strategy, the transmission power, and direction, can be tunable segmentally while maintaining high transmission efficiency. The proposed concept is verified to be effective through experiment.

This paper develops the coordination structure and method for utilizing flexibilities in a Micro-Grid (MG), an Active Distribution Network (ADN) and a Transmission Grid (TG), which can play an essential role in addressing the uncertainties caused by renewable energy power generation (REPG). For cooperative dispatching, both flexibilities and uncertainties on the interface of MG–ADN and ADN–TG are portrayed in unified forms utilizing robust optimization (RO), based on the modified equipment-level model of flexible resources. The Constraint-and-Column Generation method is adopted to solve the RO control problems. Simulations on the modified IEEE case-6 and case-33 systems are carried out. The results suggest that the proposed algorithm can exploit flexible resources in both an MG and an ADN, improving the economy and promoting REPG consumption within each level (MG, ADN and TG) while reducing uncertainties and providing flexibilities for superior operators.

This study set out to extract the charging characteristics of an electrical vehicle (EV) from massive real operating data. Firstly, an unsupervised learning method based on self-organizing map (SOM) is developed to deal with the power supply side data of various charging operators. Secondly, a multi-dimensional evaluation index system is constructed for charging operation and vehicle-to-grid (V2G). Finally, according to more than five million pieces of charging operating data collected over a period of two years, the charging load composition and characteristics under different charging station types, daily types and weather conditions are analyzed. The results show that bus, high-way, and urban public charging loads are different in concentration and regulation flexibility, however, they all have the potential to synergy with power grid and cooperate with renewable energy. Especially in an urban area, more than 37 GWh of photovoltaic (PV) power can be consumed by smart charging at the current penetration rate of EVs.

Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the ‘Electric Vehicles−Power Grid−Traffic Network’ fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners’ charging behavior.

Hybrid organic-inorganic lead halide perovskites (HOIPs) have appealed to researchers on account of excellent optoelectronic properties. Compared with films which possess grain boundaries, HOIPs single crystals with fewer defects behave excellent transport and recombination performances. In the family of HOIPs, single crystals of MAPbX _3 (MA = CH _3 NH _3 ^+ , X = Cl, Br or I) are recognized as the most competitive candidates for optoelectronic applications. However, the photodetectors based on MAPbX _3 have difficulties in detecting weak signals for lacking of gains without structure optimizations and extra energy transfer channels. In this study, taking advantage of MAPbBr _3 single crystal (100) facets, planar metal-semiconductor-metal (MSM) photodetectors were fabricated with Au zigzag electrodes and modified Au nanoparticles (NPs) to realize localized Au surface plasmons (SPs). Compared to device without Au NPs, 2 times enhancement of photocurrent and responsivity have been achieved under 630 nm photon irradiation and 5 V bias. Furthermore, the surface metal structures can inhibit ionic migration to a certain extent. Potential mechanisms of the enhancements and suppressions are discussed in details to reveal the applications of this technique.

Alcoholic liver disease (ALD) is a major cause of chronic liver disease worldwide. It is a complex process, including a broad spectrum of hepatic lesions from fibrosis to cirrhosis. Our previous study suggested that astaxanthin (AST) could alleviate the hepatic inflammation and lipid dysmetabolism induced by ethanol administration. In this study, a total of 48 male C57BL/6J mice were divided into 4 groups: a Con group (fed with a Lieber–DeCarli liquid diet), an AST group (fed with a Lieber–DeCarli liquid diet and AST), an Et group (fed with an ethanol-containing Lieber–DeCarli liquid diet), and a EtAST group (fed with an ethanol-containing Lieber–DeCarli liquid diet and AST). Then, comparative hepatic transcriptome analysis among the groups was performed by Illumina RNA sequencing. Gene enrichment analysis was conducted to identify pathways affected by the differentially expressed genes. Changes of the top genes were verified by quantitative real-time PCR (qRT-PCR) and Western blot. A total of 514.95 ± 6.89, 546.02 ± 15.93, 576.06 ± 21.01, and 690.85 ± 54.14 million clean reads were obtained for the Con, AST, Et, and EtAST groups, respectively. Compared with the Et group, 1892 differentially expressed genes (DEGs) (including 351 upregulated and 1541 downregulated genes) were identified in the AST group, 1724 differentially expressed genes (including 233 upregulated and 1491 downregulated genes) were identified in the Con group, and 1718 DEGs (including 1380 upregulated and 338 downregulated genes) were identified in the EtAST group. The enrichment analyses revealed that the chemokine signaling, the antigen processing and presentation, the nucleotide-binding and oligomerization domain (NOD)-like receptor signaling, and the Toll-like receptor signaling pathways enriched the most differentially expressed genes. The findings of this study provide insights for the development of nutrition-related therapeutics for ALD.

It takes electric vehicles (EVs) a long time to charge, which is bound to influence the charging experience of vehicle owners. At the same time, large-scale charging behavior also brings about large load pressure on, and elevates the overload risk of, the power distribution network. To solve these problems, we proposed a high-efficiency charging service system based on charging reservation and charging pile binding services. The system can shorten the average charging time of EVs and improve the average immediate utilization rate of new energy sources at charging stations (CSs). In addition, the system also guarantees that the EVs are charged within the allowable range of the capacity of the distribution network and avoids overloading of the distribution network caused by the charging of EVs. The key support for the utility of the system is rooted in the three-level CS selection model and the CS energy control algorithm (CSECA) proposed in the research. Finally, the proposed model and algorithm were verified to be valid through numerous simulation experiments.

Electric vehicle heat pumps are drawing more and more attention due to their energy-saving and high efficiency designs. Some problems remain, however, in the usage of the heat pumps in electric vehicles, such as a drainage problem regarding the external heat exchangers while in heat pump mode, and the decrease in heating performance when operated in a cold climate. In this article, an R134a economized vapor injection (EVI) heat pump system was built and tested. The drainage problem common amongst external heat exchangers was solved by an optimized 5 mm diameter tube-and-fin heat exchanger, which can meet both the needs of a condenser and evaporator based on simulation and test results. The EVI system was also tested under several ambient temperatures. It was found that the EVI was a benefit to the system heating capacity. Under a −20 °C ambient temperature, an average improvement of 57.7% in heating capacity was achieved with EVI and the maximum capacity was 2097 W, with a coefficient of performance (COP) of 1.25. The influences of injection pressure and economizer capacity are also discussed in this article.

Background: Newly graduated registered nurses face more challenges than their experienced counterparts, as they not only confront the high pressures of an increasingly complex medical environment but also need to quickly adapt to their jobs and role transitions. The emotional burden arising at this stage is referred to as transitional shock. Self‐depletion, as proposed by Baumeister, refers to the process by which individuals exhaust their internal psychological control resources when facing challenges, subsequently affecting cognition and emotion. The occurrence of transitional shock and the process of individual self‐depletion appear to be closely related. However, to our knowledge, there has been limited research exploring the occurrence of transitional shock from the perspective of self‐depletion theory. Aim: Investigating the emergence process of transition shock through the lens of self‐depletion theory entails an examination of the mechanisms by which individuals engage in self‐regulation when confronted with challenges and how transition shock manifests throughout this process. Designs: A descriptive qualitative study. Methods: Between August and November 2023, using maximum variation sampling and purposive sampling methods, 16 nurses were selected for semistructured interviews at a tertiary hospital in Suzhou, China. Results: Employing thematic analysis, three interconnected themes were identified, encompassing the entry‐level workforce challenges, the subsequent effects of energy depletion, and the sources and replenishment of energy. Conclusion: As new nurses adapt to their roles and environments, they encounter numerous pressures that markedly drain their psychological energy. This ongoing depletion of self‐regulation energy can lead to transitional shock, impulsive decision‐making, and missed nursing care. Implications for Nursing Management: Managers should implement comprehensive support strategies, including optimized work environments, enhanced training, and personal development, to help newly graduated nurses successfully transition and improve care quality and retention. [ABSTRACT FROM AUTHOR]