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As an important biomarker of neural aging, the brain age reflects the integrity and health of the human brain. Accurate prediction of brain age could help to understand the underlying mechanism of neural aging. In this study, a cross-stratified ensemble learning algorithm with staking strategy was proposed to obtain brain age and the derived predicted age difference (PAD) using T1-weighted magnetic resonance imaging (MRI) data. The approach was characterized as by implementing two modules: one was three base learners of 3D-DenseNet, 3D-ResNeXt, 3D-Inception-v4; another was 14 secondary learners of liner regressions. To evaluate performance, our method was compared with single base learners, regular ensemble learning algorithms, and state-of-the-art (SOTA) methods. The results demonstrated that our proposed model outperformed others models, with three metrics of mean absolute error (MAE), root mean-squared error (RMSE), and coefficient of determination (R2) of 2.9405 years, 3.9458 years, and 0.9597, respectively. Furthermore, there existed significant differences in PAD among the three groups of normal control (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD), with an increased trend across NC, MCI, and AD. It was concluded that the proposed algorithm could be effectively used in computing brain aging and PAD, and offering potential for early diagnosis and assessment of normal brain aging and AD.

ObjectiveThyroid disorders are prevalently diagnosed yet face significant challenges in their accurate identification in China. Predominantly, the reference intervals (RIs) currently in use across Chinese medical facilities derive from company-provided data, lacking stringent scientific validation. This practice underscores the urgent necessity for establishing tailored RIs for thyroid-related hormones, specifically tailored to the coastal area populations. Such refined RIs are imperative for empowering clinicians with the precise tools needed for the accurate diagnosis of both overt and subclinical thyroid conditions.MethodsThis investigation analyzed the medical histories of 6021 euthyroid individuals mainly from East coastal area of China between June 2019 and December 2020. The cohort comprised residents of coastal areas, focusing on extracting insights into the regional specificity of thyroid hormone levels. A thorough examination protocol was implemented, encompassing inquiries into thyroid health history, ultrasound screenings, palpations during thyroid surgery, detections of thyroid antibodies, and reviews of medication histories. Adherence to the CLSI C28-A3 guidelines facilitated the derivation of RIs for thyroid-related hormones, subsequently juxtaposed against those provided by commercial entities.ResultsThe study delineated the following gender- and age-specific RIs for Thyroid-Stimulating Hormone (TSH): for males under 50 years, 0.57-3.37; males over 50 years, 0.51-4.03; females under 50 years, 0.53-3.91; and females over 50 years, 0.63-4.31. Further analysis revealed the RIs for Free Thyroxine (FT4), Free Triiodothyronine (FT3), Total Thyroxine (TT4), and Total Triiodothyronine (TT3) amongst males and females, with notable distinctions observed between the two genders and across age brackets. These findings are in stark contrast to the standardized intervals provided by manufacturers, particularly highlighting differences in TT3 and FT3 levels between genders and a tendency for TSH levels to increase with age.ConclusionThis research successfully establishes refined RIs for thyroid-related hormones within the Chinese coastal area populations, taking into account critical demographic factors such as gender and age. These tailored RIs are anticipated to significantly enhance the diagnostic accuracy for thyroid diseases, addressing the previously noted discrepancies with manufacturer-provided data and underscoring the importance of regionally and demographically adjusted reference intervals in clinical practice.

Power transformer diagnostic methods based on traditional intelligent learning are affected by the scarcity of transformer fault data, which hinders their further application and prevents them from obtaining high diagnostic accuracy. To solve this problem, a few-shot method based on Gaussian Prototype Network (GPN) is proposed to achieve an effective and accurate diagnosis of power transformers using even a small number of fault samples. The method is an organic combination of embedding network and distance metric. The proposed approach is verified by datasets of dissolved gas and literature, which come from real power transformers and historical data. The results show that the method can achieve up to 96.7% accuracy, which is suitable for the field of power transformer fault diagnosis.

Abstract This study aimed to address the issue of larger prediction errors existing in intelligent predictive tasks related to Alzheimer’s disease (AD). A cohort of 487 enrolled participants was categorized into three groups: normal control (138 individuals), mild cognitive impairment (238 patients), and AD (111 patients) in this study. An improved multifeature squeeze-and-excitation-dilated residual network (MFSE-DRN) was proposed for two important AD predictions: clinical scores and conversion probability. The model was characterized as three modules: squeeze-and-excitation-dilated residual block (SE-DRB), multifusion pooling (MF-Pool), and multimodal feature fusion. To assess its performance, the proposed model was compared with two other novel models: ranking convolutional neural network (RCNN) and 3D vision geometrical group network (3D-VGGNet). Our method showed the best performance in the two AD predicted tasks. For the clinical scores prediction, the root-mean-square errors (RMSEs) and mean absolute errors (MAEs) of mini-mental state examination (MMSE) and AD assessment scale–cognitive 11-item (ADAS-11) were 1.97, 1.46 and 4.20, 3.19 within 6 months; 2.48, 1.69 and 4.81, 3.44 within 12 months; 2.67, 1.86 and 5.81, 3.83 within 24 months; 3.02, 2.03 and 5.09, 3.43 within 36 months, respectively. At the AD conversion probability prediction, the prediction accuracies within 12, 24, and 36 months reached to 88.0, 85.5, and 88.4%, respectively. The AD predication would play a great role in clinical applications.

Gas therapy is emerging as a highly promising therapeutic strategy for cancer treatment. However, there are limitations, including the lack of targeted subcellular organelle accuracy and spatiotemporal release precision, associated with gas therapy. In this study, we developed a series of photoactivatable nitric oxide (NO) donors NRh-R-NO (R = Me, Et, Bn, iPr, and Ph) based on an N-nitrosated upconversion luminescent rhodamine scaffold. Under the irradiation of 808 nm light, only NRh-Ph-NO could effectively release NO and NRh-Ph with a significant turn-on frequency upconversion luminescence (FUCL) signal at 740 nm, ascribed to lower N–N bond dissociation energy. We also investigated the involved multistage near-infrared-controlled cascade release of gas therapy, including the NO released from NRh-Ph-NO along with one NRh-Ph molecule generation, the superoxide anion O2⋅− produced by the photodynamic therapy (PDT) effect of NRh-Ph, and highly toxic peroxynitrite anion (ONOO‒) generated from the co-existence of NO and O2⋅−. After mild nano-modification, the nanogenerator (NRh-Ph-NO NPs) empowered with superior biocompatibility could target mitochondria. Under an 808 nm laser irradiation, NRh-Ph-NO NPs could induce NO/ROS to generate RNS, causing a decrease in the mitochondrial membrane potential and initiating apoptosis by caspase-3 activation, which further induced tumor immunogenic cell death (ICD). In vivo therapeutic results of NRh-Ph-NO NPs showed augmented RNS-potentiated gas therapy, demonstrating excellent biocompatibility and effective tumor inhibition guided by real-time FUCL imaging. Collectively, this versatile strategy defines the targeted RNS-mediated cancer therapy.

Abstract Background The intricate relationship between hypertension and chronic kidney disease (CKD) presents a global challenge for prevention of hypertension-related CKD. This study’s objective is to analyze age, gender, regional disparities, and evolving trends in the disease burden of hypertension-related CKD. We aim to estimate changing spatial and temporal trends in incidence and mortality rates, considering the socio-demographic index (SDI), to inform health strategies effectively. Method Age-standardized incidence rates (ASIR) and death rates (ASDR) were collected from the GBD 2019. Trend analysis was conducted by Joinpoint regression of ASRs from 1990 to 2019. Spatial autocorrelation analysis was performed to obtain spatial patterns. The association between SDI and burden of CKD due to hypertension was estimated using a Pearson correlation analysis. Results The global ASIR and ASDR due to hypertension-related CKD were 19.45 (95% CI, 17.85 to 21.09) and 5.88 (95% CI, 4.95 to 6.82) per 100 K population in 2019, representing increases of 17.89% and 13.29% compared to 1990, respectively. The elderly population and males were found the highest ASIR and ASDR. The high SDI region had the highest ASIRs, while low SDI regions experienced the highest ASDRs. Joinpoint regression found both global ASIR and ASDR showed increasing trends, with the highest increases observed in middle- and high-SDI regions, respectively. The SDI exhibited a positive association with ASIRs but displayed an inverse V-shaped correlation with the average annual percentage change (AAPC) of ASIRs. Spatial autocorrelation analysis revel significant positive spatial autocorrelation for the AAPC of ASDRs and ASIRs, from 1990 to 2019. Conclusions Results met the objectives, and demonstrated a rising global burden of hypertension-related CKD. Factors such as aging, gender, and regional variations should be considered when designing control measures and developing healthcare systems to effectively address the burden of this complex condition.

Abstract Condensers are often required in power generation systems to condense the exhaust steam generated at the end of the turbine. This paper will investigate the enhanced condensation process of spent steam in a parallel flow type. The operating parameters of steam temperature (Tcond), steam flow rate (Gin), cooling water temperature (Tin), and cooling water flow rate (Lw) will affect the condensation effect by changing the gas–liquid arrangement in the tower. In this experiment, an orthogonal test was designed in the Raschig ring to investigate the significance of these four operating parameters on the experimental results. Different types of packings have different condensation effects due to different accumulation methods. The same type of packing has a different surface area, resulting in different contact areas between the gas and liquid phases during condensation. In this experiment, four different packings will be investigated to find their condensation performance. This paper uses the size of subcooling (ΔT) as an indicator to evaluate the effectiveness of condensation, and the values of condensation rate (R), number of liquid phase heat transfer units (NTUL), and total volume heat transfer coefficient (Kv) are used as a reference for the effectiveness of condensation. The results show that the overall condensation effect of regular packing is better than that of random packing, The average condensation rate of regular packing is 95%, the average condensation rate of random packing is 90%, the subcooling of regular packing is about three to five smaller than that of random packing, the heat transfer coefficient of regular packing is about 1.5 times of that of random packing. And by fitting the Kv data for different packings, an empirical formula was obtained that can be used to predict the Kv size of other packings.

Magnetoencephalography (MEG) is a non-invasive technique that can precisely capture the dynamic spatiotemporal patterns of the brain by measuring the magnetic fields arising from neuronal activity along the order of milliseconds. Observations of brain dynamics have been used in cognitive neuroscience, the diagnosis of neurological diseases, and the brain-computer interface (BCI). In this study, we outline the basic principle, signal processing, and source localization of MEG, and describe its clinical applications for cognitive assessment, the diagnoses of neurological diseases and mental disorders, preoperative evaluation, and the BCI. This review not only provides an overall perspective of MEG, ranging from practical techniques to clinical applications, but also enhances the prevalent understanding of neural mechanisms. The use of MEG is expected to lead to significant breakthroughs in neuroscience.

Summary: Transposable elements (TEs) can provide ectopic promoters to drive the expression of oncogenes in cancer, a mechanism known as onco-exaptation. Onco-exaptation events have been extensively identified in various cancers, with bladder cancer showing a high frequency of onco-exaptation events (77%). However, the effect of most of these events in bladder cancer remains unclear. This study identified 44 onco-exaptation events in 44 bladder cancer cell lines in 137 RNA-seq datasets from six publicly available cohorts, with L1PA2 contributing the most events. L1PA2-SYT1, L1PA2-MET, and L1PA2-XCL1 had the highest frequency not only in cell lines but also in TCGA-BLCA samples. L1PA2-SYT1 showed significant tumor specificity and was found to be activated by CpG island demethylation in its promoter. The upregulation of L1PA2-SYT1 enhances the in vitro invasion of bladder cancer and is an independent risk factor for patient’s overall survival, suggesting L1PA2-SYT1 being an important event that promotes the development of bladder cancer.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and its onset is significantly associated with genetic factors. Being the capabilities of high specificity and accuracy, genetic testing has been considered as an important technique for AD diagnosis. In this paper, we presented an improved deep learning (DL) algorithm, namely differential genes screening TabNet (DGS-TabNet) for AD binary and multi-class classifications. For performance evaluation, our proposed approach was compared with three novel DLs of multi-layer perceptron (MLP), neural oblivious decision ensembles (NODE), TabNet as well as five classical machine learnings (MLs) including decision tree (DT), random forests (RF), gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM) and support vector machine (SVM) on the public data set of gene expression omnibus (GEO). Moreover, the biological interpretability of global important genetic features implemented for AD classification was revealed by the Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO). The results demonstrated that our proposed DGS-TabNet achieved the best performance with an accuracy of 93.80% for binary classification, and with an accuracy of 88.27% for multi-class classification. Meanwhile, the gene pathway analyses demonstrated that there existed two most important global genetic features of AVIL and NDUFS4 and those obtained 22 feature genes were partially correlated with AD pathogenesis. It was concluded that the proposed DGS-TabNet could be used to detect AD-susceptible genes and the biological interpretability of susceptible genes also revealed the potential possibility of being AD biomarkers.

IntroductionThe efficacy and safety of adjuvant capecitabine in early-stage triple-negative breast cancer remains undefined. A meta-analysis was conducted to elucidate whether capecitabine-based regimens could improve survival in early-stage triple-negative breast cancer (TNBC).MethodsThe current study searched Medline, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov proceedings up to 2023.9. Disease-free survival (DFS), overall survival (OS), and grade 3–4 adverse events (AEs) were assessed. Extracted or calculated hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs) were pooled.ResultsThe capecitabine-based regimens showed significant advantages in DFS (HR = 0.81, 95% CI: 0.73–0.90; P

DC microgrid offers high efficiency, simple control, and reduced conversion stages advantages, but suffers from induced instability caused by its low inertia. Due to the strong nonlinearity of the DC microgrid, the analysis of its large-signal stability (LSS) is a very challenging task. This paper uses the T-S fuzzy model (TSFM) method to investigate the LSS of the DC microgrid with various loads, which is connected to the AC grid. The TSFM of the DC microgrid with grid-connected voltage source converter (VSC) as the main power supply is established. The analysis of the LSS of the DC microgrid is carried out using an estimate of the domain of attraction (DOA) of the working point. On the basis of the DOA, the effects of different types of loads and system parameters on the LSS are researched. The research results can serve as a guideline for adjusting the physical and control parameters to solve the DC microgrid instability problem. Finally, the large-signal stability analysis (LSSA) of the DC microgrid is performed using the MATLAB/Simulink simulation platform. The results of the simulation confirm the rightness of the proposed LSSA approach. They indicate that constant power load (CPL) has the greatest effect on the LSS of the DC microgrid compared to the constant impedance load (CIL) and the constant current load (CCL). In addition, the results of the time-domain simulation also validate the effectiveness of this method, which is a way to enhance the LSS of the DC microgrid through the adjustment of system parameters.

Spectral CT can produce basis materials with different X-ray energies. Subsequently, the generated basis materials can be used for qualitative and quantitative evaluation of tissue components and contrast agent distribution. This approach presents a superior ability to separate and identify imaging materials compared to traditional single-energy CT. Dual-energy spectrum technology is one of the most commonly used modes in spectrum CT, which plays an important role in clinical application. In this study, the decomposition methods of a basis material in the image domain of dual-energy spectrum CT were classified into two categories: two-material decomposition and multi-material decomposition. Finally, these methods are summarized and trend of future development is addressed.

The identification of head landmarks in cephalometric analysis significantly contributes in the anatomical localization of maxillofacial tissues for orthodontic and orthognathic surgery. However, the existing methods face the limitations of low accuracy and cumbersome identification process. In this pursuit, the present study proposed an automatic target recognition algorithm called Multi-Scale YOLOV3 (MS-YOLOV3) for the detection of cephalometric landmarks. It was characterized by multi-scale sampling strategies for shallow and deep features at varied resolutions, and especially contained the module of spatial pyramid pooling (SPP) for highest resolution. The proposed method was quantitatively and qualitatively compared with the classical YOLOV3 algorithm on the two data sets of public lateral cephalograms, undisclosed anterior-posterior (AP) cephalograms, respectively, for evaluating the performance. The proposed MS-YOLOV3 algorithm showed better robustness with successful detection rates (SDR) of 80.84% within 2 mm, 93.75% within 3 mm, and 98.14% within 4 mm for lateral cephalograms, and 85.75% within 2 mm, 92.87% within 3 mm, and 96.66% within 4 mm for AP cephalograms, respectively. It was concluded that the proposed model could be robustly used to label the cephalometric landmarks on both lateral and AP cephalograms for the clinical application in orthodontic and orthognathic surgery.

Space launch vehicles are usually loaded with a large amount of propellant, and the destructive power caused by their explosion is significant. The altitude of an accidental explosion will lead to differences in the destructive power, because the environmental parameters of different altitudes are different, and the explosion shock wave parameters are closely related to the environmental parameters. Therefore, it was necessary to establish a set of analytical methods to rapidly analyze the relationship between altitude and the explosion shock wave parameters. A large number of simulation conditions were tested at 0~10 km altitude using the nonlinear dynamics software AUTODYN. The relationship between the explosion shock wave pressure P, the proportional distance z, and the altitude h was obtained, which could be used to rapidly characterize the explosion shock wave pressures at different altitudes. A comparative analysis was conducted with the experimental results to verify the scientificity of the fitting formula. The results indicate that the error between the experimental and calculated values is less than 5.80%, indicating that the rapid evaluation formula established in this paper is reasonable. These findings not only enrich the theory related to explosion shock waves, but also provide a rapid analysis basis for the accidental explosion of space launch vehicles.

The digital image method of monitoring structural displacement is receiving more attention today, especially in non-contact structure health monitoring. Some obvious advantages of this method, such as economy and convenience, were shown while it was used to monitor the deformation of the bridge structure during the service period. The image processing technology was used to extract structural deformation feature information from surveillance video images containing structural displacement in order to realize a new non-contact online monitoring method in this paper. The influence of different imaging distances and angles on the conversion coefficient (η) that converts the pixel coordinates to the actual displacement was first studied experimentally. Then, the measuring and tracking of bridge structural displacement based on surveillance video images was investigated by laboratory-scale experiments under idealized conditions. The results showed that the video imaging accuracy can be affected by changes in the relative position of the imaging device and measured structure, which is embodied in the change in η (actual size of individual pixel) on the structured image. The increase in distance between the measured structure and the monitoring equipment will have a significant effect on the change in the η value. The value of η varies linearly with the change in shooting distance. The value of η will be affected by the changes in shooting angle. The millimeter-level online monitoring of the structure displacement can be realized using images based on surveillance video images. The feasibility of measuring and tracking structural displacement based on surveillance video images was confirmed by a laboratory-scale experiment.

Abstract Exhaust steam condensation equipment is an important component in thermal power generation systems. Direct‐contact condensation offers high heat transfer efficiency, small flow resistance, simple structure, and less scaling; therefore, its application to exhaust steam condensation equipment is conducive to reducing equipment investment and operation costs. In this study, the direct‐contact condensation of subatmospheric pressure steam (exhaust steam) is investigated in a cocurrent flow packed tower. The effects of steam temperature, steam flow, cooling water temperature, and cooling water flow on the condensation rate, subcooling, number of liquid‐phase heat transfer units (NTUL), and total volume heat transfer coefficient (KV) are investigated. The results show that the direct‐contact condensation of exhaust steam can yield a high condensation rate, low subcooling, and good stability in the cocurrent flow packed tower. Lower steam temperature, higher steam flow, higher inlet‐water temperature, and lower water flow are conducive to the increase in NTUL. It is discovered that NTUL and KV can be expressed by dimensionless parameters of flow and temperature, and that the empirical correlations of NTUL and KV agree well with experimental data.

The coupling between functional and structural brain networks is difficult to clarify due to the complicated alterations in gray matter and white matter for the development of Alzheimer's disease (AD). A cohort of 112 participants [normal control group (NC, 62 cases), mild cognitive impairment group (MCI, 31 cases) and AD group (19 cases)], was recruited in our study. The brain networks of rsfMRI functional connectivity (rsfMRI-FC) and diffusion tensor imaging structural connectivity (DTI-SC) across the three groups were constructed, and their correlations were evaluated by Pearson's correlation analyses and multiple comparison with Bonferroni correction. Furthermore, the correlations between rsfMRI-SC/DTI-FC coupling and four neuropsychological scores of mini-mental state examination (MMSE), clinical dementia rating-sum of boxes (CDR-SB), functional activities questionnaire (FAQ) and montreal cognitive assessment (MoCA) were inferred by partial correlation analyses, respectively. The results demonstrated that there existed significant correlation between rsfMRI-FC and DTI-SC (p < 0.05), and the coupling of rsfMRI-FC/DTI-SC showed negative correlation with MMSE score (p < 0.05), positive correlations with CDR-SB and FAQ scores (p < 0.05), and no correlation with MoCA score (p > 0.05). It was concluded that there existed FC/SC coupling and varied network characteristics for rsfMRI and DTI, and this would provide the clues to understand the underlying mechanisms of cognitive deficits of AD.

Sub-moderate temperature (ranging from low to moderate temperature) anaerobic digestion (AD) could balance fermentation efficiency and energy input. We investigated biogas production and the microbial community in wastewater AD at sub-moderate (15 °C, 20 °C, and 25 °C) and moderate (35 °C; control group) temperatures with the organic loading rate (OLR) incrementally increased over 200 days. The impact of temperature on biogas production was found to be minimal at a low OLR but became more significant at a high OLR. Notably, a temperature threshold ranging from 15 °C to 20 °C exerted a strong inhibitory effect on biogas production and disrupted the microbial community. And, SMT-AD is deemed by this study to be the optimal application strategy of wastewater with low temperature and low OLR. Bacterial richness was positively and linearly related to temperature. There is a relevance between methane production and archaeal diversity under the influence of temperature and OLR. Temperature and OLR shaped the ecological function of predominant bacteria. Anaerolineales, Thermotogales, and Lactobacillales were strongly influenced by temperature. Synergistales had a synergistic relationship with Desulfovibrionales. Clostridiales was responsible for acetate and butyrate production and closely related to Lactobacillales. Acetoclastic Methanosaetaceae was the predominant methanogen. Methanogens could survive and maintain their population even though methanogenesis was limited under high OLRs and low temperatures.

This study uses a 2D hydro-morphological model to analyze hydrodynamics over flat and deformed beds with a near-bank vegetation patch. By varying the patch density, the generalized results show that the hydrodynamics over deformed beds differs a lot from those over flat beds. It is found that the deformed bed topography leads to an apparent decrease in longitudinal velocity and bed shear stress in the open region and longitudinal surface gradient for the entire vegetated reach. However, the transverse flow motion and transverse surface gradient in the region of the leading edge and trailing edge is enhanced or maintained, suggesting the strengthening of secondary flow motion. Interestingly, the deformed bed topography tends to alleviate the horizontal shear caused by the junction-interface horizontal coherent vortices, indicating that the turbulence-induced flow mixing is highly inhibited as the bed is deformed. The interior flow adjustment through the patch for the deformed bed requires a shorter distance, La, which is related to the vegetative drag length, (Cda)−1, with a logarithmic formula (La = 0.4ln[(Cda)−1] + b, with b = 3.83 and 4.03 for the deformed and flat beds). The sloping bed topographic effect in the open region accelerating the flow may account for the quick flow adjustment. HIGHLIGHTS Near-bank vegetation results in a pool-riffle system.; A 2D hydro-geomorphological model reproduced the deformed bed induced by near-bank vegetation.; Hydrodynamics over flat and deformed beds are compared.; Interior flow adjustment is shortened due to the formation of a deformed bed.; Horizontally-dimensional turbulence is inhibited by the deformed bed.;

Abstract Objective To investigate whether surgery improves prognosis in elderly patients with Merkel cell carcinoma (MCC). Materials/Methods Data of all patients with MCC diagnosed between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Differences in baseline characteristics were analyzed among the age groups (75–80, 80–85, and ≥85 years). Multivariate Cox proportional hazards analysis was used to assess the effects of each variable on patient outcomes. The Kaplan–Meier curves were employed to evaluate MCC overall survival (OS) and MCC‐specific survival (MSS). Results A total of 1156 of patients with MCC met the inclusion and exclusion criteria. The surgery rate decreased with age (75–80, 80–85, and ≥85 years were 93.3%, 91.1%, and 88.7%, respectively; p = 0.082). Multivariate Cox proportional hazards analysis showed that the OS of patients in the 80–85 years group (hazard ratio [HR] = 1.39; 95% confidence interval [CI] = 1.14–1.70; p = 0.001) and the ≥85 years group (HR = 2.18; 95% CI = 1.80–2.63; p

For the solar photovoltaic (PV) system to operate efficiently, it is necessary to effectively establish an equivalent model of PV cell and extract the relevant unknown model parameters accurately. This paper introduces a new metaheuristic algorithm, i.e., gaining-sharing knowledge based algorithm (GSK) to solve the solar PV model parameter extraction problem. This algorithm simulates the process of knowledge acquisition and sharing in the human life cycle and is with strong competitiveness in solving optimization problems. It includes two significant phases. The first phase is the beginner–intermediate or junior acquisition and sharing stage, and the second phase is the intermediate–expert or senior acquisition and sharing stage. In order to verify the effectiveness of GSK, it is applied to five PV models including the single diode model, double diode model, and three PV modules. The influence of population size on the algorithm performance is empirically investigated. Besides, it is further compared with some other excellent metaheuristic algorithms including basic algorithms and advanced algorithms. Among the five PV models, the root mean square error values between the measured data and the calculated data of GSK are 9.8602E−04 ± 2.18E−17, 9.8280E−04 ± 8.72E−07, 2.4251E−03 ± 1.04E−09, 1.7298E−03 ± 6.25E−18, and 1.6601E−02 ± 1.44E−16, respectively. The results show that GSK has overall better robustness, convergence, and accuracy.

The properties exhibited by Ni-rich cathode materials were enhanced through the mixed coating layers of Li3PO4 and boric acid. The scanning electron microscopy (SEM), the transmission electron microscope (TEM), the differential scanning calorimetry (DSC), the Electrochemical Impedance Spectroscopy (EIS), as well as the half-cell and full-cell charge-discharge tests were adopted for characterizing the structure and electrochemical properties exhibited by the cathode materials. As revealed by results, the Li3PO4 and boric mixed coating layers can effectively reduce the surface area and protect the direct contact between cathode material particle surface and electrolyte, meanwhile improving the structural stability and cycle performance. The coating of fast ionic conductor contributes to enhance the specific capacity possessed by the Ni-rich cathode materials accordingly.

A domino reaction from 2-acetylfuran/2-acetylthiophene, benzaldehydes and sulfur powder has been developed to synthesize a series of tetrahydrothiophene derivatives. The reaction proceeds well to construct five new bonds and a tetrahydrothiophene ring by one-pot. A possible mechanism was proposed, involving a stepwise of Aldol/double Michael addition/internal SN2 cascaded reaction with sulfur powder acts as a source of sulfur. This method is characterized by mild reaction conditions, commercially available starting materials and transition-metal-free.

Soybean oil body (SOB) emulsions were prepared using OBs extracted at pH 11.0 and pH 7.0. The pH 11.0-SOB comprised oleosins, whereas pH 7.0-SOB comprised extrinsic proteins and oleosins. All SOB emulsions were heated at 60–100 °C for 15 min. Heating may lead to the release of extrinsic proteins from the surface of pH 7.0-SOB due to heat-induced denaturation. The total proportion of α-helix and β-sheets gradually decreased from 77 (unheated) to 36.2% (100 °C). During stomach digestion, the extrinsic protein hydrolysis of heated pH 7.0-SOB emulsions was fast between 60 and 80 °C, and it then slowed between 90 and 100 °C; heating inhibited the oleosin hydrolysis of pH 7.0- and 11.0-SOBs. Heat treatment promoted aggregation and coalescence, and it resulted in increased particle sizes for all emulsions. Larger aggregates were found in heated pH 7.0-SOB emulsions, and larger oil droplets were found in heated pH 11.0-SOB emulsions. After intestinal digestion, the droplets of all SOB emulsions gradually dispersed, and particle sizes decreased. Different heating temperatures had lesser effects on particle sizes and microstructures. Lipolysis was affected by the extraction pH and heating. For pH 11.0-SOB emulsions, the FFA release tendency was greatly affected by the heating temperature, and heating to 80 °C resulted in the highest FFA release (74%). However, all pH 7.0-SOB emulsions had similar total FFA releases. In addition, the droplet charges of heated pH 7.0-SOB emulsions were lower than those of unheated pH 7.0-SOB emulsions in both the intestine and stomach phases; however, the charge changes in different pH 11.0-SOB emulsions showed the opposite tendency. This study will offer guidance regarding the application of SOB emulsions in food.

The approach of graph-based diffusion tensor imaging (DTI) networks has been used to explore the complicated structural connectivity of brain aging. In this study, the changes of DTI networks of brain aging were quantitatively and qualitatively investigated by comparing the characteristics of brain network. A cohort of 60 volunteers was enrolled and equally divided into young adults (YA) and older adults (OA) groups. The network characteristics of critical nodes, path length (Lp), clustering coefficient (Cp), global efficiency (Eglobal), local efficiency (Elocal), strength (Sp), and small world attribute (σ) were employed to evaluate the DTI networks at the levels of whole brain, bilateral hemispheres and critical brain regions. The correlations between each network characteristic and age were predicted, respectively. Our findings suggested that the DTI networks produced significant changes in network configurations at the critical nodes and node edges for the YA and OA groups. The analysis of whole brains network revealed that Lp, Cp increased (p < 0.05, positive correlation), Eglobal, Elocal, Sp decreased (p < 0.05, negative correlation), and σ unchanged (p ≥ 0.05, non-correlation) between the YA and OA groups. The analyses of bilateral hemispheres and brain regions showed similar results as that of the whole-brain analysis. Therefore the proposed scheme of DTI networks could be used to evaluate the WM changes of brain aging, and the network characteristics of critical nodes exhibited valuable indications for WM degeneration.

Objective: This work aimed to verify the candidate biomarkers for keloid disorder (KD), and analyze the role of immune cell infiltration (ICI) in the pathology of keloid disorder.Methods: The keloid-related datasets (GSE44270 and GSE145725) were retrieved from the Gene Expression Omnibus (GEO). Then, differential expressed genes (DEGs) were identified by using the “limma” R package. Support vector machine-recursive feature elimination (SVM-RFE) and LASSO logistic regression were utilized for screening candidate biomarkers of KD. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic power of candidate biomarkers. The candidate biomarkers were further verified through qRT-PCR of keloid lesions and the matched healthy skin tissue collected from eight cases. In addition, ICI in keloid lesions was estimated through single-sample gene-set enrichment analysis (ssGSEA). Finally, the potential drugs to the treatment of KD were predicted in the Connectivity Map Database (CMAP).Results: A total of 406 DEGs were identified between keloid lesion and healthy skin samples. Among them, STC2 (AUC = 0.919), SDC4 (AUC = 0.970), DAAM1 (AUC = 0.966), and NOX4 (AUC = 0.949) were identified as potential biomarkers through the SVM-RFE, LASSO analysis and ROC analysis. The differential expressions of SDC4, DAAM1, and NOX4 were further verified in collected eight samples by qRT-PCR experiment. ICI analysis result showed a positive correlation of DAAM1 expression with monocytes and mast cells, SDC4 with effector memory CD4+ T cells, STC2 with T follicular helper cells, and NOX4 with central memory CD8+ T cells. Finally, a total of 13 candidate small molecule drugs were predicted for keloids treatment in CMAP drug database.Conclusion: We identified four genes that may serve as potential biomarkers for KD development and revealed that ICI might play a critical role in the pathogenesis of KD.

Health status assessment is the key link of transformer-condition-based maintenance. The health status assessment method of power transformers mostly adopts the method based on the health index, which has the problems of multiple parameters of each component and strong subjectivity in the selection of weight value, which is easily causes misjudgment. However, the existing online monitoring system for dissolved gas in transformer oil (DGA) can judge the normal or abnormal state of the transformer according to the gas concentration in a monitoring cycle. Still, there are problems, such as fuzzy evaluation results and inaccurate judgment. This paper proposes a dynamic state evaluation method for power transformers based on the Mahalanobis–Taguchi system. First, the oil chromatography online monitoring time series is used to screen key features using the Mahalanobis–Taguchi system to reduce the problem of excessive parameters of each component. Then, a Mahalanobis distance (MD) calculation is introduced to avoid subjectivity in weight selection. The health index (HI) model of a single transformer is built using the MD calculated from all DGA data of a single transformer. Box–Cox transformation and 3 σ criteria determine the alert value and threshold value of all transformer His. Finally, taking two transformers as examples, we verify that the proposed method can reflect the dynamic changes of transformer operation status and give early warning on time, avoiding the subjectivity of parameter and weight selection in the health index, which easily causes misjudgment and other problems and can provide a decision-making basis for transformer condition-based maintenance strategies.

Energy conservation and emission reduction is a critical task for China's aluminum industry. During the last decade, the high temperature superconducting (HTS) technology has been successfully demonstrated to have great potential for energy conservation in this field. Different from the present AC induction heater, this HTS direct current (DC) induction heater is expected to significantly improve the energy efficiency of preheating aluminium billets before extrusion process. This paper thoroughly presents the development of the world's first commercial megawatt HTS DC inducting heater based on 2nd generation HTS tapes. Firstly, the commercial design of this 1MW HTS DC induction heater including design specifications, energy efficiency and commercial potential are analyzed carefully. Subsequently, the manufacture and operation of three core parts including HTS magnet with extra-large diameter, iron core structure and electrical driving system, are introduced. A low cost and practical driving system consisting of motors and gearboxes is developed to solve the problem of peak electromagnetic torque at low rotation. Meanwhile, the heating quality of aluminium billets (i.e., temperature distribution along the radial and axial direction) can be accurately controlled by an adjustable air gap structure. Finally, the present technique challenges and corresponding solutions of this commercial HTS DC induction heaters are summarized and proposed. Compared with an energy efficiency of 45-60% in the MW-class conventional heater, the energy efficiency of this HTS DC induction heater can achieve up to 80.6% based on test results. This work can also provide guidance for the design and optimization of HTS DC induction heaters.

Parameter extraction of solar cell models plays an important role in the simulation, evaluation, control, and optimization of the photovoltaic (PV) system. Although many meta-heuristic algorithms have been proposed to solve the parameter extraction, it is necessary to further improve the accuracy and reliability of these algorithms. In this paper, an optimized teaching-learning-based optimization (TLBO) is proposed, namely dynamic self-adaptive and mutual-comparison teaching-learning-based optimization (DMTLBO). DMTLBO enhances the basic TLBO by improving its teacher phase and learner phase: (i) In the teacher phase, two differentiated and personalized teaching strategies are proposed according to learners’ learning status. In these two strategies, an adaptive state transition weight factor $\omega $ and a dynamic gap weight factor $\beta $ are introduced to reflect the dynamic transformation of the learners’ learning state in the actual teaching situation. (ii) In the learner phase, a new learning strategy is proposed. The learner can communicate and learn with three different learners who are randomly selected and ranked. To verify the performance of the DMTLBO algorithm, it is used to extract the parameters of different PV models, such as the single diode model, the double diode model, and three PV modules. Among these PV models, the root mean square error values between the measured data and the calculated data of DMTLBO are 9.8602E-04 ± 2.07E-17, 9.8248E-04 ± 1.53E-06, 2.4251E-03 ± 2.15E-17, 1.7298E-03 ± 5.74E-14, and 1.6601E-02 ± 4.55E-10, respectively. Compared with other optimization algorithms, the experimental results show that DMTLBO can provide better or highly competitive convergence speed and extraction accuracy. Besides, the influence of the improved teacher phase and learner phase on DMTLBO and the changing process of both weight factors $\omega $ and $\beta $ are investigated.

Background Machine learning (ML) algorithms have been widely used in the classification of white blood cells (WBCs). However, the performance of ML algorithms still needs to be addressed for being short of gold standard data sets, and even the implementation of the proposed algorithms.Methods In this study, the method of two-module weighted optimized deformable convolutional neural networks (TWO-DCNN) was proposed for WBC classification. Our algorithm is characterized as two-module transfer learning and deformable convolutional (DC) layers for the betterment of robustness. To validate the performance, our method was compared with classical MLs of VGG16, VGG19, Inception-V3, ResNet-50, support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT) and random forest (RF) on our undisclosed WBC data set and public BCCD data set.Results TWO-DCNN achieved the best performance with the precisions (PREs) of 95.7%, 94.5% and 91.6%, recalls (RECs) of 95.7%, 94.5% and 91.6%, F1-scores (F1s) of 95.7%, 94.5% and 91.6%, area under curves (AUCs) of 0.98, 0.97 and 0.95 for low-resolution and noisy undisclosed data sets, BCCD data set, respectively.Conclusions With accurate feature extraction and optimized network weights, the proposed TWO-DCNN showed the best performance in WBC classification for low-resolution and noisy data sets. It could be used as an alternative method for clinical applications.

Little is known about whether the application of biogas residues in rice fields will affect the degradation of pesticides. This study investigated the dissipation behavior of the fungicide difenoconazole in paddy water and sediment after the application of a chemical fertilizer or biogas residues. The results showed that the application of biogas residues changes the dissipation of difenoconazole in both paddy water and sediment. The half-lives of difenoconazole in paddy water and sediment with biogas residues were 0.50 and 10.09 days, respectively, while the half-lives of difenoconazole in paddy water and sediment with chemical fertilizer were only 0.22 and 4.64 days, respectively. After biogas residues were applied in a paddy field, no significant changes in pH value of paddy water and sediment and soil microorganisms were observed, but soil organic matter decreased by 30%. The above studies suggest that biogas residues may affect the dissipation of pesticides in paddy field water and sediments, which provides a new focus on the scientific and rational use of biogas residues as organic fertilizer in rice fields.

In order to improve the efficiency of the conventional AC induction heater, an HTS DC induction heating technology has been proposed. A 1MW HTS DC induction heater has been developed, and the efficiency of the HTS DC induction heater can be more than 80% due to the HTS DC magnet with no AC loss. The electromagnetic and thermal analysis of the HTS DC induction heater is necessary for further optimization of the 1MW HTS DC induction heater. A 3D finite element simulation model for the coupling analysis of electromagnetic and thermal has been built based on the actual 1MW HTS DC induction heater and optimized for accelerating the computing speed by using permanent magnet instead of the HTS magnet. The heating process of the cylindrical aluminum billet has also been simulated and compared with the experimental results. The results show that the simulation results can be in line with the experimental results. What is more, the optimized simulation model can be used to further optimize the device parameters for different workpiece temperature distributions, or design and evaluate the new HTS DC induction heater in the future.

Under the increasingly stringent environmental regulations, the installed capacity of distributed renewable energy is increasing rapidly. How to fully absorb renewable energy without affecting power grid security and ensuring power quality is the key problem to be solved. The rapid development of energy production, conversion and storage equipment of prosumer makes the operation of micro energy grid possible. Therefore, according to the energy characteristics of different prosumer, this article divides the micro energy grid into two types: micro energy grid of electricity selling and micro energy grid of energy supplying. The constrained nonlinear optimization method is used to solve the optimization problem of this high-dimensional nonlinear system with time delay. In this article, the comparative study of optimal operation control between the two kinds of micro energy grid is carried out. In order to deal with the uncertainty of distributed renewable energy output and load forecasting deviation, this article proposes a real time adaptive dynamic optimization control strategy based on deep learning. The strategy uses deep learning technology to pretrain the action network, so as to learn the optimal operation behavior of micro energy grid. Finally, the online simulation of micro energy grid for consecutive days is used to verify the correctness and real-time performance of the algorithm.

Microgrid energy management is a typical discrete non-linear optimization problem that is usually solved by off-line optimization, day-ahead demand-side management, and long-term dynamic optimization scheduling strategy. However, due to the intermittent distributed generation and time-varying load in microgrids, more attention should be paid to the real-time optimal scheduling of the overall operation of energy to ensure the dynamic balance of supply and demand in microgrids. Combining demand-side response with real-time power price, this paper applies the strategy to microgrid energy management and proposes a distributed energy real-time management model of microgrid based on demand-side response function. A deep adaptive dynamic programming optimization algorithm is also proposed for the model. The real-time interaction between microgrid operators and users is realized. The closed-loop feedback control structure of the proposed model ensures the real-time optimization control strategy. Therefore, the proposed energy management model and control strategy can realize intra-day dispatching in microgrids. The real-time performance and effectiveness of the proposed energy management model and control strategy are also verified by numerical simulation. Finally, since the proposed model is approximate, whether the solution obtained by the algorithm is the optimal or satisfactory solution of the optimization strategy set is a lack of theoretical support. Therefore, according to the approximation theorem of bounded rationality, the application conditions of the model in power markets are proposed. It is proved that the proposed model meets the application conditions, and is a specific application of bounded rationality approaching complete rationality in the power market. It is also proved that the best solution is involved in the satisfactory solution set of the model. Thus, the control strategy is a rational and feasible optimal management control strategy, which provides a theoretical basis for its further implementation.

Equivalent hydraulic aperture and fracture surface roughness are two significant factors affecting the fluid flow behaviors in rock fractures. To understand the role of fracture surface roughness and aperture in the fluid flow through 3D self-affine rough fractures, roughness fracture surfaces with joint roughness coefficients equal to 2.5, 7.5, 12.5, and 17.5 were established, and the Navier–Stokes equation was used to compute the fluid flow in these 3D self-affine rough fractures with a mechanical aperture increase from 0.2 mm to 0.8 mm with a gradient of 0.2 mm. The results show that when the fracture mechanical aperture is 0.2 mm, the impact of fracture surface roughness on fluid flow is considerable, while this effect decreases obviously with the increase of fracture mechanical aperture. Comparing the permeability obtained by the Navier–Stokes equation with the cube law under different hydraulic gradients, we found that their deviation increased with the increase of hydraulic gradients. This allows for the definition of a critical hydraulic gradient (Jc), below which the permeability can be properly predicted using the cubic law for its simplicity, and above which Forchheimer’s equation should be applied, and Forchheimer’s coefficients A, B, and Jc can be calculated by the prediction equations established in this study.

Compared with traditional rule-based algorithms, deep reinforcement learning methods in autonomous driving are able to reduce the response time of vehicles to the driving environment and fully exploit the advantages of autopilot. Nowadays, autonomous vehicles mainly drive on urban roads and are constrained by some map elements such as lane boundaries, lane driving rules, and lane center lines. In this paper, a deep reinforcement learning approach seriously considering map elements is proposed to deal with the autonomous driving issues of vehicles following and obstacle avoidance. When the deep reinforcement learning method is modeled, an obstacle representation method is proposed to represent the external obstacle information required by the ego vehicle input, aiming to address the problem that the number and state of external obstacles are not fixed.

Abstract Herein, N-Ti3C2@CNT microspheres are successfully synthesized by the simple spray drying method. In the preparation process, HCl-treated melamine (HTM) is selected as the sources of carbon and nitrogen. It not only realizes in situ growth of CNTs on the surface of MXene nanosheets with the catalysis of Ni, but also introduces efficient N-doping in both MXene and CNTs. Within the microsphere, MXene nanosheets interconnect with CNTs to form porous and conductive network. In addition, N-doped MXene and CNTs can provide strong chemical immobilization for polysulfides and effectively entrap them within the porous microspheres. Above-mentioned merits enable N-Ti3C2@CNT microspheres to be ideal sulfur host. When used in lithium–sulfur (Li–S) battery, the N-Ti3C2@CNT microspheres/S cathode delivers initial specific capacity of 927 mAh g−1 at 1 C and retains high capacity of 775 mAh g−1 after 1000 cycles with extremely low fading rate (FR) of 0.016% per cycle. Furthermore, the cathode still shows high cycling stability at high C-rate of 4 C (capacity of 647 mAh g−1 after 650 cycles, FR 0.027%) and high sulfur loading of 3 and 6 mg cm−2 for Li–S batteries.

The use of the conventional dual closed-loop control strategy by photovoltaic (PV) plants with grid-connected inverters may weaken the damping of a power system, which may aggravate low-frequency oscillations (LFOs). This influence will become more severe as the penetration of PV plants increases. Therefore, it is necessary to incorporate damping controls into PV plants to suppress LFOs. This paper proposed an additional compound damping control (ACDC) system that combines additional damping control (ADC) for the inverter with ADC-based dynamic power compensation control (DPCC), allowing hybrid energy storage systems (HESSs) to suppress LFOs. First, the feasibility of suppressing low-frequency oscillations in PV plants is demonstrated by the torque method and a small signal model. Then, an additional damping controller is added to the active power control link of the PV inverter to enhance the damping abilities of the system. However, given that the damping performance of PV plants with only ADC is limited by the compensated power, PV plants require devices that can rapidly compensate for the damping power. Therefore, we added the HESS to the DC bus and proposed DPCC. Finally, a three-machine nine-node system for a PV plant was modeled and simulated in the PSCAD platform. The simulation results showed that the proposed control strategy could provide effective damping for interarea oscillation.

A flash flood disaster is one of the most destructive natural disasters. With the increase in extreme rainfall events, more and more areas will be threatened by flash floods. The flash flood susceptibility assessment is the basis of flash flood risk assessment and is also an important step in flash flood disaster management. Based on Citespace analysis tools, this study made a bibliometric and visualized analysis of 305 documents collected in the core collection of Web of Science in the past 15 years, including the analysis of the number of publications and citation frequency, influence analysis, keyword analysis, author co-citation analysis, and institutional co-operation analysis. This paper summarizes the current research status and future development trend of flash flood susceptibility assessment from five key research subfields, including assessment scale, assessment unit, assessment index, assessment model, and model assessment method, discusses the analysis of the application of remote sensing and GIS in flash flood susceptibility assessment, discusses the problems encountered in the current research of the five subfields, and provides suggestions for flash flood hazard control.

Abstract Background This study aimed to elucidate the effects and mechanisms of Radix Linderae (RL) extracts on a mouse model of diabetic bladder dysfunction (DBD), especially on later decompensated phase. Methods Male C57BL/6J mice were intraperitoneally injected with streptozotocin (STZ) after 4 weeks of high-fat diet (HFD) feeding. DBD mouse models (later decompensated phase) were developed by 12-weeks persistent hyperglycemia and then treated with RL extracts for 4 weeks. During administration, the fasting blood glucose (FBG) test was performed once a week. Four weeks later, oral glucose tolerance test (OGTT), voided stain on paper (VSOP), and urodynamic alteration were explored. We also performed haematoxylin and eosin (H&E) and Masson’s trichrome staining to observe the histology of the bladder. Then, the contractile responses to α, β-methylene ATP, capsaicin (CAP), KCl and carbachol were measured. Moreover, qPCR assay was performed to analyse the bladder gene expression levels of M3 receptors and TRPV1. Results The diabetic mice exhibited higher FBG, OGTT and urine production, and no substantial alteration was observed after RL treatment. Urodynamic test showed the maximum bladder capacity (MBC), residual volume (RV) and bladder compliance (BC), as well as the decrement of voided efficiency (VE) and micturition volume (MV), remarkably increased in the DBD mice. Furthermore, RL treatment significant improved urodynamic urination, with lower MBC, RV, and, BC, as well as higher VE and MV, as compared with the model groups. The wall thickness of the bladder and the ratio of smooth muscle/collagen remarkably increased, and RL could effectively attenuate the pathological change. The response of bladder strips to the stimulus was also reduced in the DBD mice, and RL treatment markedly increased the contraction. Furthermore, the gene expression levels of M3 receptors and TRPV1 were down-regulated in the bladders of the diabetic mice, whereas RL treatment retrieved those gene expression levels. Conclusions RL extracts can improve the bladder voiding functions of the DBD model mice in later decompensated phase, and underlying mechanisms was associated with mediating the gene expression of M3 receptors and TRPV1 in the bladder instead of improving blood sugar levels.

This study investigated the effects of naringin on soybean and mung bean sprouts postharvest quality. It was found that naringin could maintain the appearance and quality of soybean sprouts and mung bean sprouts during a 6-day storage period as well as delay the occurrence of browning in mung bean sprouts and soybean sprouts. The optimal application rate of naringin was 50–100 μg/mL, which could effectively inhibit the activity of polyphenol oxidase (PPO) and peroxidase (POD) in bean sprouts and increase the ascorbic acid content, where this inhibition response to the browning of mung bean sprouts and soybean sprouts was significantly reduced. Naringin treatment increased gallic acid and p-coumaric acid content in mung bean sprouts as well as the daidzin and rutin content in soybean sprouts, which was also reflected in the improvement of antioxidant activity. The binding of naringin with PPO and POD was analyzed with molecular docking, naringin, and PPO had a lower binding energy (−1.09 Kcal/mol). In conclusion, naringin application in postharvest preservation of mung bean sprouts and soybean sprouts can maintain favorable consumer quality.

Flexible interconnection transformation of a distribution network using all-control power electronics technology will help to overcome the technical bottleneck of distributed generation (DG) penetration and high-quality power supply in the traditional distribution network. However, the flexible interconnection form that uses back-to-back power electronic converters to replace tie switches has disadvantages such as high cost and large footprint, so its mass promotion and application are bound to be limited. Therefore, a new topology of flexible interconnected distribution network with embedded DC system (FDN+EDC) with a better economy is proposed. Firstly, the topology of FDN+EDC is introduced to improve the controllability level of the distribution network under high percentage DG penetration, and is compared with the conventional flexible interconnection method. Secondly, in order to realize the coordinated optimal economic operation of source, network and load in the FDN+EDC, a dynamic reconfiguration model of switches status and continuous adjustment of flexible interconnection device (FID) are proposed. Then, to achieve a fast and globally optimal solution of the model, a hierarchical coordinated optimization strategy is designed based on an improved particle swarm algorithm for the optimization of discrete and continuous variables. Finally, the validity of the algorithm is verified, and an 88-node FDN+EDC is constructed to verify the advantages of the proposed embedded DC flexible interconnection topology and realize the joint optimal dispatch of network-source-load.

In recent years, many retailers sell their products through not only offline but also online platforms. The sales of perishable goods on e-commerce platforms recorded phenomenal growth in 2020. However, some retailers are overconfident and order more products than the optimal ordering quantity, resulting in great losses due to product decay. In this paper, we apply the newsvendor model to analyze the impacts of overconfident behavior on the retailer’s optimal pricing and order quantity decisions and profit. Our model provides the overconfident retailer with a feasible and effective method to adjust optimal ordering and pricing decisions. Through numerical studies, we examine the retailer’s optimal decisions under the scenarios of complete rationality, over-estimation, and over-precision. We find that the over-estimation retailer always orders more products than the optimal order quantity, and the over-precision retailer always orders fewer products than the optimal order quantity. Under some conditions, overconfidence hurts the retailer’s revenue to a large extent. Therefore, it is beneficial for the overconfident retailer to adjust its order quantity according to our research findings.

In the process of power transformer risk assessment, the loss degree index is difficult to accurately quantify due to the influence of uncertain factors, leading to the deviation of risk judgment. A power transformer risk assessment method based on the three-parameter interval grey number decision-making is proposed. Firstly, the fault probability of the transformer is quantified based on the condition evaluation results. Secondly, considering the uncertainty of DG output and load, the Nataf transform and Cholesky decomposition were used to eliminate the correlation of random variables, and a three-point estimation method combined with a DC cut load model was introduced to calculate the probability distribution of the loss degree caused by the transformer fault. Finally, the origin moment of each order was obtained based on the calculation formula of risk value, and the risk probability distribution was obtained through the Cornish–Fisher series expanding. The decision method of the three-parameter interval grey number distance measure was used to judge the risk grade of the equipment. The results show that the proposed method fully considers the influence of uncertainty on equipment risk judgment, can realize the full use of the equipment risk value interval number to judge the risk, and avoids the decision-making defects of the traditional certain risk quantification method. Meanwhile, the influence of different factors on the risk evaluation results is in line with the actual operation condition of the transformer. The results also verify the effectiveness and accuracy of the proposed method, which provides a new judgment idea for power grid equipment risk quantitative assessment.

Objective: Diabetic bladder dysfunction (DBD) is one of the most common and bothersome complications of diabetes mellitus (DM). The purpose of the present study is to investigate DBD in KK-Ay mice, and to identify the expression of relative genes. Method: Totally twenty-seven KK-Ay mice and thirty C57BL/6 J mice, respectively, were randomly divided into 12-, 18-, and 25-week old groups. The weight, water intake, voided volume, the frequency of micturition, fasting blood glucose (FBG), oral glucose tolerance test (OGTT) were measured at varying time points. Maximum bladder volume (MBC), residual volume (RV), bladder compliance (BC), micturition efficiency (VE) and maximum micturition pressure (MVP) were assessed by urodynamic test, and contractile responses to α, β‐methylene ATP, KCl, electrical‐field stimulation, carbachol were performed by detrusor smooth muscle strips contractility test. The bladders were stained with hematoxylin and eosin (H&E) and Masson’s trichrome to determine bladder wall thickness. Additionally, the mRNA expression of Myosin Va, SLC17A9, P2X1, M3 and M2 were then verified by qRT-PCR. Result: The weight, water intake, voided volumes, micturition frequency, FBG, the blood glucose AUC0-2h of KK-Ay mice were significantly increased at three time points. MBC, RV and BC were significantly increased; VE was significantly lower at the age of 18 and 25 weeks in KK-Ay mice; MVP was significantly increased at the age of 25 weeks in KK-Ay mice. In DSM strips contractility test, the amplitude of the spontaneous activity in KK-Ay mice significant increased at 12 weeks and 18 weeks, while both the amplitude and frequency were significantly decreased at the age of 25 weeks. The level of Myosin Va, SLC17A9 and M3 receptor significantly decreased in KK-Ay mice at 12 weeks, while Myosin Va markedly increased at 18 weeks; P2X1 and M2 receptors of KK-Ay mice was significantly increased at all three time points. Conclusion: Taken together, this study demonstrates that KK-Ay mice can be a proper model to investigate DBD whose transformation from compensatory state to decompensated state may ascribe to the time-dependent alternations of Myosin Va, SLC17A9, P2X1, M3 and M2 expression levels.

Skid-steered wheeled vehicles are commonly adopted in outdoor environments with the benefits of mobility and flexible structure. However, different from Ackerman turning vehicles, skid-steered vehicles do not possess geometric constraint but only dynamic constraint when steered, which leads to motion control and state estimation problems for skid-steered vehicles. The controlling accuracy of a skid-steered vehicle depends largely on feedback state information from sensors and an observer. In this study, a 3-DOF dynamic model using a Brush nonlinear tire model is built, first, to model a 6 × 6 skid-steered wheeled vehicle in flat ground driving conditions. Then, an observer using the unscented Kalman filter with a strong tracking algorithm and adaptive noise matrix adjustment (AN-STUKF) is established to estimate vehicle motion states based on the 3-DOF dynamic model. Finally, the experiment is carried out in three different driving conditions to verify the accuracy and stability of the proposed method. The results show that the AN-STUKF method possesses better accuracy and tracking rate than the traditional UKF, and the phenomenon of ICRs shifting forward of the skid-steered wheeled vehicle is also verified.

Pomelo is rich in bioactive compounds (carotenoids, phenolics and essential oil) in the early stage of fruit development, but it is often wasted in the cultivation and management process. To gain an insight into the carotenoid metabolism pathway in pomelo, the carotenoid profiles and the expression patterns of carotenogenic genes were investigated in two genotypes of pomelo during early fruit development. The results showed that a higher carotenoid content was observed in honey pomelo as compared with golden pomelo, which may be related to different gene regulation mechanisms. Lutein, α-carotene, and β-carotene were the main carotenoids in pomelo young fruit, and lutein was the highest one. The accumulation of carotenoids during fruit early development in honey pomelo is related to the transcriptional regulation of ZISO and LUT5. In golden pomelo, the rate-limiting gene for carotenoids is PDS and ZDS. In addition, the expression of seven genes except CRTISO in honey pomelo was higher than that in golden pomelo. The results are helpful to further clarify the regulatory mechanism of carotenoid accumulation during early fruit development and provide a direction for the high-value utilization of young fruits in pomelo.

Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.