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Objectives: To explore the value of multiple metabolic and heterogeneity parameters of 2-deoxy-2-[fluorine-18] fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) in predicting epidermal growth factor receptor gene (EGFR) mutations in non-small cell lung cancer (NSCLC). Materials and methods: A retrospective analysis was performed by reviewing 98 patients with NSCLC who underwent EGFR mutation testing and 18F-FDG PET/CT examination in our hospital between March 2016 and March 2021. Patients were divided into an EGFR-mutant group and a wild-type group. A multivariate logistic regression analysis was performed to screen and construct a prediction model. The diagnostic performance of the model was evaluated using a receiver-operating characteristic (ROC) curve. Results: The study found that EGFR mutations were more likely to occur in women, non-smokers, and patients with peripheral lesions, shorter maximum tumor diameter, adenocarcinoma, and T1 stage cancer. Low maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume, total lesion glycolysis, and high coefficient of variation (COV) were significantly correlated with EGFR mutations, and the area under the ROC curve (AUC) was 0.622, 0.638, 0.679, 0.687, and 0.672, respectively. Multivariate logistic regression analysis indicated that non-smokers (odds ratio (OR) = 0.109, P = 0.014), peripheral lesions (OR = 6.917, P = 0.022), low SUVmax (≤ 7.85, OR = 5.471, P = 0.001), SUVmean (≤ 5.34, OR = 0.044, P = 0.000), and high COV (≥ 106.08, OR = 0.996, P = 0.045) were independent predictors of EGFR mutations. The AUC of the prediction model established by combining the above factors was 0.926; the diagnostic efficiency was significantly higher than that of a single parameter. Conclusion: Among the metabolic and heterogeneity parameters of 18F-FDG PET/CT, low SUVmax, SUVmean, and high COV were significantly associated with EGFR mutations, and the predictive value of EGFR mutations could be enhanced when combined with clinicopathological features. [ABSTRACT FROM AUTHOR]

Ambient air pollution is a leading cause of non-communicable disease in the world. PM2.5 has the potential to change the miRNAs profiles, which in turn causes cardiovascular effects. Hypoxia-inducible factor (HIF)-1 plays a critical role in the development of atherosclerosis. Yet, the possible role of miR-939-5p/HIF-1α in PM2.5-induced endothelial injury remains elusive. Therefore, the study aims to investigate the effects of miR-939-5p and HIF-1α on PM2.5-triggered endothelial injury. The results from immunofluorescence, qRT-PCR, LSCM, and western blot assays demonstrated that PM2.5 increased the levels of HIF-1α, inflammation and apoptosis in human aortic endothelial cells (HAECs). Yet, the inflammatory response and mitochondrial-mediated apoptosis pathway were effectively inhibited in HIF-1α knockdown HAECs lines. The expression of miR-939-5p was significantly down-regulated in HAECs after exposed to PM2.5. The luciferase reporter, qRT-PCR and western blot results demonstrated that miR-939-5p could directly targeted HIF-1α. And the miR-939-5p overexpression restricted PM2.5-triggered decreases in cell viability and increases in lactic dehydrogenase (LDH) activity, reactive oxygen species (ROS), mitochondrial membrane potential (MMP) and inflammation. In addition, miR-939-5p overexpression remarkably suppressed PM2.5-triggered BcL-2/Bax ratio reduction and Cytochrome C, Cleaved Caspase-9 and Cleaved Caspase-3 expression increase, revealed that miR-939-5p hampered PM2.5-induced endothelial apoptosis through mitochondrial-mediated apoptosis pathway. Our results demonstrated that PM2.5 increased the expression of HIF-1α followed by a pro-inflammatory and apoptotic response in HAECs. The protective effect of miR-939-5p on PM2.5-triggered endothelial cell injury by negatively regulating HIF-1α. miR-939-5p might present a new therapeutic target for PM2.5 induced endothelial injury. [ABSTRACT FROM AUTHOR]

Summary: A dynamic coupled electro‐thermal model including the impact of the state of charge (SOC), inner temperature and current flux on resistance and heat generation rate is proposed for prismatic batteries, including the ohmic and polarization resistances and entropy coefficient. This model reflects the interaction between generation rate of heat and temperature distribution and is appropriate to be employed. Subsequently, the dynamic model is implemented to reveal the temperature increase of a Li‐ion prismatic battery with the capacity of 50‐Ah under the conditions of static and dynamic currents. Experiments are performed to demonstrate the model which is given in this article can precisely describe the thermal behavior under the conditions of static and dynamic currents and different ambient temperatures, with an average relative error of 5.87% for the static condition and of 11.81% for the dynamic condition. In addition, comparative studies are utilized to decide the application range of the dynamic model. It shows according to the results that it should be used for the temperature prediction under the condition of dynamic current. As for the static current condition, the static model ignoring the effect of current can replace the dynamic model, although the temperature prediction precision of the dynamic model is slightly higher than that of the static model. Finally, the proposed dynamic model is compared with a resistance‐based thermal model where the heat generation depends only on SOC and is demonstrated to be superior to its counterpart. [ABSTRACT FROM AUTHOR]

Purpose: Advanced gastric cancer (AGC) and primary gastric lymphoma (PGL) are the two most common malignant tumors of the stomach. Conventional imaging examinations have difficulty distinguishing the two. This study explored the values of multiple parameters and visual assessment of 18F-fluorodeoxyglucose(18F-FDG) uptake heterogeneity of positron emission tomography/computed tomography(PET/CT) for differentiating between AGC and PGL. Methods: This retrospective study included 70 AGC and 26 PGL patients, all of whom had undergone 18F-FDG PET/CT before treatment. We analyzed the differences between AGC and PGL in the distribution of metastatic lesions and multiple metabolic parameters, including the maximum standardized uptake value (SUVmax), SUVmax/maximal thickness(THKmax), metabolic tumor volume and total lesion glycolysis (TLG). In addition, 18F-FDG uptake heterogeneity was visually assessed using a visual scoring method and a method of measuring SUVmax differences (SUVmax-d). Results: The most common metastasis of AGC patients were liver, bone, peritoneal and proximal lymph nodes; PGL patients had fewer peritoneal metastases and lymph node metastasis could appeared to be "skip metastasis." The metabolic parameters—SUVmax, SUVmax/THKmax and TLG—were higher in patients who had PGL, especially in diffuse large B-cell lymphoma (DLBCL). In the visual assessment of 18F-FDG uptake heterogeneity, the measurements of SUVmax-d in PGL were significantly higher than in AGC. Receiver operating characteristics curve analysis suggested that SUVmax has the highest comprehensive diagnostic efficiency due to having the highest value of area under the curve and the highest accuracy (77.2%). Conclusion: 18F-FDG PET/CT had a high diagnostic efficiency for discrimination of AGC and PGL, especially between DLBCL and other pathological subtypes. Visual assessment used to evaluate 18F-FDG uptake heterogeneity could help to distinguish the two types of tumors. In addition, our innovative method of measuring the heterogeneity of 18F-FDG uptake—namely, SUVmax-d—could contribute to identification of the two tumor types and should have its significance clarified by future studies. [ABSTRACT FROM AUTHOR]

Summary: This work establishes an improved electrothermal‐coupled model for the estimation of the temperature evolution in an air‐cooled pack with three parallel branches and four serial cells in each branch. This model includes the influences of the cells' state of charge (SOC) and temperature on the ohmic and polarization resistances and polarization capacitance. The current distribution in the pack is considered in the model and applied to predicting the inconsistent effect of cell temperature. Moreover, the pipe network theory is used to model the airflow in the pack and the heat convection between the air and the batteries. An experiment is implemented to verify prediction precision in the electrical and thermal parameters of the pack. The results show that the electrothermal model accurately estimates the electrical and thermal performance of the air‐cooled pack. The relative error of the pack terminal voltage between the prediction and the experiment is 3.22% under the conditions of a discharging rate is 1.5 C (C denotes the ratio of charging/discharging current to battery capacity), environment temperature of 37°C, and air inlet velocity of 6 m/s. Regarding the prediction error in the temperature, the root mean square errors of most batteries are no more than 0.6°C under the conditions of discharge rates of 1 C and 1.5 C and ambient temperatures of 17°C, 27°C, and 37°C. [ABSTRACT FROM AUTHOR]

[Display omitted] • The value of transfer learning has been assessed for fault diagnosis in building energy systems. • Four popular model-based transfer learning implementation strategies have been investigated. • Transfer learning can improve the fault diagnosis performance when the modeling data are limited. • The maximum fault diagnosis accuracy improvements are 12.63% and 8.18% in two learning tasks respectively. • The data amount of source domain affects the stability of the transfer learning significantly. Data-driven fault detection and diagnosis (FDD) in building energy systems is typically limited by the quantity and quality of training data. These methods can be only used for individual systems due to the insufficient extrapolation capabilities of most machine learning algorithms. A desirable solution is to utilize transfer learning, which can transfer the knowledge learned from data-rich building energy systems to FDD tasks in data-sparse systems. However, the potential of applying transfer learning to such FDD has not been systematically investigated. Accordingly, this paper proposes a transfer-learning-based methodology for fault diagnosis in building chillers. Experiments were conducted on two water-cooled screw chillers to collect both fault and fault-free data. Transfer-learning-based fault diagnosis experiments were implemented with consideration of different transfer learning tasks, training cases, learning scenarios, and transfer learning implementation strategies. The experimental results validate the value of transfer learning for FDD in building energy systems, especially when the experimental data available for model development are limited. The maximum accuracy improvements were 12.63% and 8.18% in the two learning tasks. The research outcomes provide practical guidelines for developing transfer-learning-based solutions for FDD in building energy systems. [ABSTRACT FROM AUTHOR]

This study investigates the corrosion behavior of Ni-Cr binary alloys, including Ni-10Cr, Ni-15Cr, Ni-20Cr, Ni-25Cr, and Ni-30Cr, in a NaCl-KCl-MgCl2 molten salt mixture through gravimetric analysis. Corrosion tests were conducted at 700 °C, with the maximum immersion time reaching up to 100 h. The corrosion rate was determined by measuring the mass loss of the specimens at various time intervals. Verifying corrosion rates by combining mass loss results with the determination of element dissolution in molten salts using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Detailed examinations of the corrosion products and morphology were conducted using X-ray diffraction (XRD) and scanning electron microscopy (SEM). Micro-area elemental analysis on the corroded surfaces was performed using an energy dispersive spectrometer (EDS), and the elemental distribution across the corrosion cross-sections was mapped. The results indicate that alloys with lower Cr content exhibit superior corrosion resistance in the NaCl-KCl-MgCl2 molten salt under an argon atmosphere compared to those with higher Cr content; no corrosion products were retained on the surfaces of the lower Cr alloys (Ni-10Cr, Ni-15Cr). For the higher Cr alloys (Ni-20Cr, Ni-25Cr, Ni-30Cr), after 20 h of corrosion, a protective layer was observed in certain areas. The formation of a stable Cr2O3 layer in the initial stages of corrosion for high-Cr content alloys, which reacts with MgO in the molten salt to form a stable MgCr2O4 spinel structure, provides additional protection for the alloys. However, over time, even under argon protection, the MgCr2O4 protective layer gradually degrades due to chloride ion infiltration and chemical reactions at high temperatures. Further analysis revealed that chloride ions play a pivotal role in the corrosion process, not only facilitating the destruction of the Cr2O3 layer on the alloy surfaces but also possibly accelerating the corrosion of the metallic matrix through electrochemical reactions. In conclusion, the corrosion behavior of Ni-Cr alloys in the NaCl-KCl-MgCl2 molten salt environment is influenced by a combination of factors, including Cr content, chloride ion activity, and the formation and degradation of protective layers. This study not only provides new insights into the corrosion resistance of Ni-Cr alloys in high-temperature molten salt environments but also offers significant theoretical support for the design and optimization of corrosion-resistant alloy materials. [ABSTRACT FROM AUTHOR]

Timely and accurate fault diagnosis (FD) in building energy systems (BESs) can promote energy efficiency and sustainable development. Especially the heating, ventilating, and air-conditioning (HVAC) systems are diverse and operate under complex and variable operation conditions. System and operation differences lead to great differences in operational data which causes poor adaptability of data-driven FD models that are developed using data from a single HVAC system or limited operation condition. To improve diagnostic performance across different HVAC systems and operation conditions, this study proposes high-adaptability FD models using three deep transfer learning (DTL) strategies including network-based fine-tuning (FT), mapping-based domain-adaptive neural network (DaNN) and adversarial-based domain adversarial neural network (DANN). The effectiveness of DTL-based FD is validated by fault datasets of two typical BESs: one is a 703-kW screw chiller while the other is the 316-kW centrifugal chiller from ASHRAE RP-1043. Two types of TL scenarios (cross-system and cross-operation-condition fault diagnosis) are set up consisting of eight TL tasks. For DTL strategies, both FD performance and transferability are evaluated using metrics like accuracy and accuracy improvement degree (AID). Results indicate that FT obtains 93% FD accuracy averagely for all tasks of the two TL scenarios considered, which is an average 55% AID compared with the non-transfer benchmark model convolutional neural network (CNN). Further, the impacts of source and target data volumes, and TL tasks are analyzed. For cross-operation-condition scenario, DTL-based FD accuracy grows with the increase of target data volume. For cross-system scenario, FT still show high FD performance with less training data. The reason why FT outperforms DANN and DaNN is explained by visualizing classification scatterplots of the last NN layers. Practical application issues of the DTL-based FD strategy for building energy systems are discussed at last. [Display omitted] • Proposed 3 different DTL strategies to validate BES cross-system and cross-operation-condition FD. • The 3 DTL chiller FD strategies: FT outperforms DANN and DaNN with 93% FD accuracy. • FT gets 55% average FD accuracy improvement for 8 cross-system and cross-operation-condition tasks. • DTL gets higher FD accuracy with growing target data for cross-operation-conditions scenario. • FT learns general features in bottom NN layers while specific ones related to the FD task in top layers. [ABSTRACT FROM AUTHOR]

[Display omitted] • 3 DTL strategies obtain 75% PIRs than LSTM for data shortage BEP tasks in OFs and UCs. • Recommend Fine-tune for next few weeks BEP by balance between time cost and performance. • Recommend DANN for nearly a year BEP by outperformed accuracy than Fine-tune, DaNN. • Evaluate DTL prediction accuracy by varying available training data in both source and target buildings. Performing accurate building energy prediction (BEP) is one of the most important foundations for achieving energy resource allocation and developing energy efficiency measures. Buildings are diverse and operate under complex conditions leading to distribution differences of energy-related data among different buildings. Owing to such differences, the already reported data-driven BEP models exhibit poor cross-building prediction performance since they only use insufficient operation data of a single building. Although several deep transfer learning (DTL) strategies have been applied with improved cross-building prediction performance, there is still a lack of comparison of various DTL strategies, which would help to determine the optimal DTL strategy for different scenarios. Hence, three DTL strategies were compared: network-based Fine-tune, adversarial-based domain adversarial neural network (DANN), and mapping-based domain adaptive neural network (DaNN). The usefulness of the DTL strategy was validated using the open source dataset Building Data Genome Project 2 in the scenario of insufficient available data for real buildings. The influences of several factors were analysed, such as the available data volumes of the source and target domains within the training set. The applicability of different DTL strategies was discussed considering both accuracy and computational cost. Results show that the three DTL strategies outperform the traditional long short-term memory (LSTM) with an average BEP performance improvement ratio (PIR) of 0.75. For the extremely limited amount of available training data scenario, Fine-tune was recommended for the next-few-weeks prediction owing to its good balance between time cost and prediction performance. For the data shortage scenario of BEP tasks for nearly a year, DANN was recommended owing to its outperforming prediction accuracy. This provides insights for practical applications of DTL during the development of BEP models for cross-building BEP tasks without sufficient operation data. [ABSTRACT FROM AUTHOR]

Current research on chiller fault detection and diagnosis (FDD) is usually based on the multi-classification method for mixed multiple fault types. The establishment of a multi-classification FDD model requires obtaining extensive test data of normal operation and multiple common faults at different fault levels, so the cost-to-benefit ratio will not be accepted. In addition, because of the information interference between different types of fault data, FDD model is hard to avoid over-fitting or under-fitting, and there may be an intolerable high false alarm rate existing in product application. Therefore, it is difficult to promote FDD in actual chiller product applications. This paper proposes an inverse fault detection and diagnosis (IFDD) strategy, which is based on the characteristics of the target fault (the fault to be concerned) to establish a two-classification fault diagnosis model. Compared with the multi-classification method, the amount of fault test data required by the two-classification methods to realize fault diagnosis can be greatly reduced. With IFDD strategy, the detection and diagnosis are completed simultaneously, while fast calculation speed, high diagnosis accuracy rate and low false alarm rate are achieved. The core of the IFDD strategy is to concentrate resources to capture the characteristics of the target fault, and no longer classify other types of faults and normal operating conditions, so it can avoid the influence of information interference between multiple types of fault data in the fault diagnosis model. In order to verify the feasibility of this strategy, a 200 ton variable-speed screw chiller has been tested on the laboratory, and a database of normal operation, refrigerant leakage, refrigerant overcharge faults, and a new operating condition of insufficient cooling water flow is initially established. Based on the sensors configured by the chiller unit and using the exponentially weighted moving-average (EWMA) control chart for IFDD, the model's fault diagnosis accuracy for the target fault reaches 100%, and no false alarm appears after the transition period. [ABSTRACT FROM AUTHOR]