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Abstract Photothermal therapy is favored by cancer researchers due to its advantages such as controllable initiation, direct killing and immune promotion. However, the low enrichment efficiency of photosensitizer in tumor site and the limited effect of single use limits the further development of photothermal therapy. Herein, a photo-responsive multifunctional nanosystem was designed for cancer therapy, in which myeloid-derived suppressor cell (MDSC) membrane vesicle encapsulated decitabine-loaded black phosphorous (BP) nanosheets (BP@ Decitabine @MDSCs, named BDM). The BDM demonstrated excellent biosafety and biochemical characteristics, providing a suitable microenvironment for cancer cell killing. First, the BDM achieves the ability to be highly enriched at tumor sites by inheriting the ability of MDSCs to actively target tumor microenvironment. And then, BP nanosheets achieves hyperthermia and induces mitochondrial damage by its photothermal and photodynamic properties, which enhancing anti-tumor immunity mediated by immunogenic cell death (ICD). Meanwhile, intra-tumoral release of decitabine induced G2/M cell cycle arrest, further promoting tumor cell apoptosis. In vivo, the BMD showed significant inhibition of tumor growth with down-regulation of PCNA expression and increased expression of high mobility group B1 (HMGB1), calreticulin (CRT) and caspase 3. Flow cytometry revealed significantly decreased infiltration of MDSCs and M2-macrophages along with an increased proportion of CD4+, CD8+ T cells as well as CD103+ DCs, suggesting a potentiated anti-tumor immune response. In summary, BDM realizes photothermal therapy/photodynamic therapy synergized chemotherapy for cancer.

Background: Bronchiectasis has high rates of hemoptysis and recurrent hemoptysis, which is inconsistent among various etiologies. Idiopathic bronchiectasis and post-tuberculous bronchiectasis are two important etiologies in China, but the differences in clinical features and risk factors of recurrent hemoptysis have not been elucidated. Methods: Patients hospitalized for idiopathic bronchiectasis or post-tuberculosis bronchiectasis were included. Patients were followed up for at least 24 months post-BAE. Demographic characteristics and clinical data were collected and analyzed between idiopathic bronchiectasis and post-tuberculosis bronchiectasis. Based on the outcomes of recurrent severe hemoptysis in patients with post-tuberculosis bronchiectasis, Cox regression models were used to identify risk factors for recurrence. Results: Among 417 patients including 352 idiopathic bronchiectasis and 65 post-tuberculous bronchiectasis, 209 (50.1%) were females. Compared with the idiopathic group, the proportion of patients with female (54.5% vs. 26.2%, p

This study proposes a hybrid static economic dispatch (HSED) model that incorporates multiple constraints specific to power systems to enhance the economic efficiency of power dispatch following the integration of wind energy. Wind energy integration in power systems can effectively reduce operational costs and energy consumption. However, the significant influx of renewable energy sources can introduce system instability and complicate power dispatch procedures. An improved pathfinder algorithm (IPFA) is proposed to address the cost minimization problem associated with power dispatch involving wind energy. The HSED model contains constraints related to wind energy penetration rate, operating area limitations, and slope rate, thereby ensuring its applicability in real-world power dispatch scenarios. The IPFA incorporates three measures such as Kent mapping initialization, nonlinear adaptation factor, and following correction strategy. The result indicates that the IPFA achieves a reduction of up to 95.86($/h) and 606.24($/h) in operating costs compared to alternative methods when wind energy is not considered. The IPFA reduces operating costs by up to 9.3% and 7.5% compared to scenarios without wind energy when wind energy is integrated. The proposed model and method contribute to enhance renewable energy utilization while simultaneously ensuring the power system economic feasibility and stability.

The effect of nitrogen (N) fertilizer on the development of maize kernels has yet to be fully explored. MicroRNA-mRNA analyses could help advance our understanding of how kernels respond to N. This study analyzed the morphological, physiological, and transcriptomic changes in maize kernels under different N rates (0, 100, 200, and 300 kg ha–1). The result showed that increasing N application significantly increased maize grains’ fresh and dry weight until N reached 200 kg ha–1. Higher levels of indole-3-acetic acid, cytokinin, gibberellin, and a lower level of ethylene were associated with increased N applications. We obtained 31 differentially expressed genes (DEGs) in hormone synthesis and transduction, and 9 DEGs were regulated by 14 differentially expressed microRNAs (DEMIs) in 26 pairs. The candidate DEGs and DEMIs provide valuable insight for manipulating grain filling under different N rates.

Abstract Background In recognizing the need for a reliable and valid instrument to assess psychological distress among children and adolescents, the present study translated the newly developed Depression Anxiety Stress Scale for Youth (DASS-Y) into Chinese, while also comparing its psychometric properties with those of the well-established DASS-21 within a primary and middle school demographic. Methods Utilizing a combination of convenience sampling and purposive sampling, a cohort comprising 1,507 primary and 1,131 middle school students was recruited. Rasch analysis, confirmatory factor analysis (CFA), and structural equation modeling (SEM) were used in the data analysis. Results Significant differences were observed between the DASS-Y and the DASS-21, notably within the anxiety subscale. The proportions of individuals with clinical mood disorders identified by the two scales demonstrated a significant disparity. Both scales, following an adjustment of responses, exhibited satisfactory internal consistency and convergent validity, with the acceptance of a three-factor structure. Furthermore, the DASS-Y showed superior discriminant validity relative to the DASS-21, providing more compelling evidence regarding concurrent validity. Conclusion Overall, the Chinese version of the DASS-Y demonstrated superior robustness to the DASS-21 in terms of psychometric properties. The findings provide initial evidence for the psychometric properties of the DASS-Y from another culture.

ObjectiveWe aimed to investigate the value of contrast-enhanced ultrasound (CEUS) in the preoperative prediction of the histological grades and molecular subtypes of breast cancer.MethodsA total of 183 patients with pathologically confirmed breast cancer were included. Contrast enhancement patterns and quantitative parameters were compared in different groups. The receiver operating characteristic (ROC) curve was used to analyze the efficacy of CEUS in the preoperative prediction of pathological characteristics, including histologic grade and molecular subtypes.ResultsHeterogeneous enhancement, perfusion defects, and peripheral radial vessels were mostly observed in higher histologic grade (grade III) breast cancer. Heterogeneous enhancement and perfusion defect were the most effective indicators for grade III breast cancer, with the areas under the ROC curve of 0.768 and 0.756, respectively. There were significant differences in the enhancement intensity, post-enhanced margin, perfusion defects, and peripheral radial vessel among the different molecular subtypes of breast cancer (all P < 0.01). Perfusion defects and clear edge after enhancement were the best qualitative criteria for the diagnosis of HER-2 overexpressed and triple-negative breast cancers, and the corresponding areas under the ROC curves were 0.804 and 0.905, respectively. There were significant differences in PE, WiR, WiPI, and WiWoAUC between grade III vs grade I and II breast cancer (P < 0.05). PE, WiR, WiPI, and WiWoAUC had good efficiency in the diagnosis of high-histologic-grade breast cancer. PE had the highest diagnostic efficiency in Luminal A, while WiPI had the highest diagnostic efficiency in Luminal B subtype breast cancer, and the areas under the ROC curve were 0.825 and 0.838, respectively. WiWoAUC and WiR were the most accurate parameters for assessing triple-negative subtype breast cancers, and the areas under the curve were 0.932 and 0.922, respectively.ConclusionQualitative and quantitative perfusion analysis of contrast-enhanced ultrasound may be useful in the non-invasive prediction of the histological grade and molecular subtypes of breast cancers.

IntroductionIn light of the significant impact that teachers have on education quality and student growth, their mental health warrants special attention. With the increasing popularity of Information and Communication Technology (ICT) and the rise of online teaching during the pandemic, teachers have become a group prone to developing problematic smartphone use (PSU). Psychological need thwarting (PNT) has been shown to be closely related to PSU, psychological distress, and perceived administrative support. However, most previous studies have adopted a variable-centered approach, which may overlook the possibility that the three basic needs are not closely associated and could form distinct profiles. Therefore, this study aims to apply latent profile analysis to identify different PNT profiles and their associations with PSU, psychological distress, and perceived administrative support.MethodsA longitudinal survey was conducted using convenience and purposive sampling methods. The survey involved 1,642 primary and middle school teachers working in China over a two-month interval, with the first assessment in November 2021 (Time 1) and the second in January 2022 (Time 2).ResultsThe results indicate that a three-profile model, intricately based on the PNT data gathered at Time 1, is most optimal: Class 1 is labeled as ‘High autonomy-High competence and Moderate relatedness thwarting’, Class 2 as ‘High autonomy-High competence and High relatedness thwarting’, and Class 3 as ‘Low psychological needs thwarting’. Distinct associations were observed among the three profiles concerning PSU, psychological distress, and perceived administrative support. Specifically, in terms of PSU, the score of Class 2 was higher than Class 1, with that of Class 3 being the lowest at Time 1, while at Time 2 no significant difference was found between any two of these three groups; in terms of distress, the scores of the three profiles were arranged from high to low as Class 2, 1, and 3 at both time points; and in terms of perceived administrative support, the order was just the opposite, with 3, 1, and 2 from high to low at both Time 1 and Time 2.ConclusionNotably, the consistent ranking of the three classes in terms of psychological distress and administrative support suggests a lasting influence of PNT. Future studies should explore this enduring impact further by employing additional longitudinal data sets and examining potential mediators or moderators beyond the current study’s scope.

The fully mulched ridge–furrow (FMRF) system has been widely used on the semi-arid Loess Plateau of China due to its high maize (Zea mays L.) productivity and rainfall use efficiency. However, high outputs under this system led to a depletion of soil moisture and soil nutrients, which reduces its sustainability in the long run. Therefore, it is necessary to optimize the system for the sustainable development of agriculture. The development, yield-increasing mechanisms, negative impacts, optimization, and their relations in the FMRF system are reviewed in this paper. We suggest using grain and forage maize varieties instead of regular maize; mulching plastic film in autumn or leaving the mulch after maize harvesting until the next spring, and then removing the old film and mulching new film; combining reduced/no-tillage with straw return; utilizing crop rotation or intercropping with winter canola (Brassica campestris L.), millet (Setaria italica), or oilseed flax (Linum usitatissimum L.); reducing nitrogen fertilizer and partially replacing chemical fertilizer with organic fertilizer; using biodegradable or weather-resistant film; and implementing mechanized production. These integrations help to establish an environmentally friendly, high quality, and sustainable agricultural system, promote high-quality development of dryland farming, and create new opportunities for agricultural development in the semi-arid Loess Plateau.

Abstract Background Nearly half of bronchiectasis patients receiving bronchial artery embolization (BAE) still have recurrent hemoptysis, which may be life-threatening. Worse still, the underlying risk factors of recurrence remain unknown. Methods A retrospective cohort was conducted of patients with idiopathic bronchiectasis who received BAE from 2015 to 2019 at eight centers. Patients were followed up for at least 24 months post BAE. Based on the outcomes of recurrent hemoptysis and recurrent severe hemoptysis, a Cox regression model was used to identify risk factors for recurrence. Results A total of 588 individuals were included. The median follow-up period was 34.0 months (interquartile range: 24.3–53.3 months). The 1-month, 1-year, 2-year, and 5-year cumulative recurrent hemoptysis-free rates were 87.2%, 67.5%, 57.6%, and 49.4%, respectively. The following factors were relative to recurrent hemoptysis: 24-h sputum volume (hazard ratio [HR] = 1.99 [95% confidence interval [95% CI]: 1.25–3.15, p = 0.015]), isolation of Pseudomonas aeruginosa (HR = 1.50 [95% CI: 1.13–2.00, p = 0.003]), extensive bronchiectasis (HR = 2.00 [95% CI: 1.29–3.09, p = 0.002]), and aberrant bronchial arteries (AbBAs) (HR = 1.45 [95% CI: 1.09–1.93, p = 0.014]). The area under the receiver operating characteristic curve of the nomogram was 0.728 [95% CI: 0.688–0.769]. Conclusions Isolation of Pseudomonas aeruginosa is an important independent predictor of recurrent hemoptysis. The clearance of Pseudomonas aeruginosa might effectively reduce the hemoptysis recurrence rate.

Abstract Background Pulmonary arteriovenous malformations (PAVMs) are rare but cause various manifestations. Although the diameter of feeding arteries has been linked to treatment decisions, relationships among the characteristics of PAVMs, clinical symptoms, and treatment effect remain unclear. The present study was performed to investigate how collective characteristics of PAVMs relate to clinical symptoms and to provide proper treatment recommendations for patients with PAVMs. Methods We retrospectively analysed 838,447 patients’ radiographic data and medical records from January 2018 to December 2020. Patients were included if a PAVM was radiographically detected for the first time in our hospital. Ordered multivariable logistic regression and hierarchical multiple regression were performed to analyse the relationships between characteristics of PAVMs and various clinical symptoms. We investigated the management of PAVMs in four tertiary university hospitals. Results Detection rate of PAVMs was 0.025% (207/838,447), and 37.6% of patients (78/207) also had hereditary haemorrhagic telangiectasia. Eight patients were diagnosed with bilateral diffuse PAVMs. Two hundred thirty-six lesions were detected in 199 patients, and the mean diameter of the feeding artery was 4.13 ± 1.92 mm. Most PAVMs were the simple type and located in the peripheral pulmonary area. In total, 34.3% of patients (71/207) were symptom-free; remaining patients showed various manifestations, and respiratory symptoms were most common (dyspnoea on exertion, 47.8%). The diameter of the feeding artery and the type and the number of PAVMs were correlated with hypoxaemia (P

Abstract Neolamarckia cadamba is an important tropical and subtropical tree for timber industry in southern China and is also a medicinal plant because of the secondary product cadambine. N. cadamba belongs to Rubiaceae family and its taxonomic relationships with other species are not fully evaluated based on genome sequences. Here, we report the complete sequences of mitochondrial genome of N. cadamba, which is 414,980 bp in length and successfully assembled in two genome circles (109,836 bp and 305,144 bp). The mtDNA harbors 83 genes in total, including 40 protein-coding genes (PCGs), 31 transfer RNA genes, 6 ribosomal RNA genes, and 6 other genes. The base composition of the whole genome is estimated as 27.26% for base A, 22.63% for C, 22.53% for G, and 27.56% for T, with the A + T content of 54.82% (54.45% in the small circle and 54.79% in the large circle). Repetitive sequences account for ~ 0.14% of the whole genome. A maximum likelihood (ML) tree based on DNA sequences of 24 PCGs supports that N. cadamba belongs to order Gentianales. A ML tree based on rps3 gene of 60 species in family Rubiaceae shows that N. cadamba is more related to Cephalanthus accidentalis and Hymenodictyon parvifolium and belongs to the Cinchonoideae subfamily. The result indicates that N. cadamba is genetically distant from the species and genera of Rubiaceae in systematic position. As the first sequence of mitochondrial genome of N. cadamba, it will provide a useful resource to investigate genetic variation and develop molecular markers for genetic breeding in the future.

Abstract Background The life-threatening haemorrhagic complications of pulmonary arteriovenous malformations (PAVMs) are extremely rare, and only described in isolated cases. This study was designed to comprehensively investigate management of ruptured PAVMs. Methods We retrospectively assessed clinical and imaging data of ruptured PAVMs to summarize incidence, clinical characteristics, and outcomes following embolisation between January 2008 and January 2021. Results Eighteen of 406 (4.4%) patients with PAVMs developed haemorrhagic complications. Twelve of 18 patients were clinically diagnosed with hereditary haemorrhagic telangiectasia (HHT). Haemorrhagic complications occurred with no clear trigger in all cases. Eight of 18 patients (44.4%) were initially misdiagnosed or had undergone early ineffective treatment. 28 lesions were detected, with 89.3% of them located in peripheral lung. Computed tomography angiography (CTA) showed indirect signs to indicate ruptured PAVMs in all cases. Lower haemoglobin concentrations were associated with the diameter of afferent arteries in the ruptured lesions. Successful embolotherapy was achieved in all cases. After embolotherapy, arterial oxygen saturation improved and bleeding was controlled (P

Abstract Background Fecundity is important for farm blue fox (Vulpes lagopus), who with asthenospermia have be a problem in some of farms in China. A key symptom of asthenospermia is decreased sperm motility. The decreased secreting beta-defensin108 (vBD108) of blue fox is speculated be related to asthenospermia. To clarify this idea, the mRNA expression of vBD108 in testis and epididymis of blue foxes with asthenospermia were detected and compared to the healthy one. The antibody was prepared and analyzed by immunohistochemistry. Results The vBD108 in testis and epididymis was found both in blue fox with asthenospermia and healthy group by the method of immunohistochemistry. The expression of vBD108 mRNA in testes (P

ObjectivesThe purpose of this study was to establish a nomogram for predicting cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC).Materials and MethodsA total of 418 patients with papillary thyroid carcinoma undergoing total thyroidectomy with cervical lymph node dissection were enrolled in the retrospective study from January 2016 to September 2019. Univariate and multivariate Logistic regression analysis were performed to screen the clinicopathologic, laboratory and ultrasound (US) parameters influencing cervical lymph nodes metastasis and develop the predicting model.ResultsCLNM was proved in 34.4% (144/418) of patients. In the multivariate regression analysis, Male, Age < 45 years, Tumor size > 20mm, multifocality, ambiguous boundary, extracapsular invasion and US-suggested lymph nodes metastasis were independent risk factors of CLNM (p < 0.05). Prediction nomogram showed an excellent discriminative ability, with a C-index of 0.940 (95% confidence interval [CI], 0.888-0.991), and a good calibration.ConclusionThe established nomogram showed a good prediction of CLNM in patients with PTC. It is conveniently used and should be considered in the determination of surgical procedures.

The 17 spent fluid catalytic cracking refinery catalysts (SFCCCs) from different petroleum refineries were collected and the leachates of SFCCCs were prepared. The ecotoxicity of SFCCC leachates to Raphidocelis subcapitata was assayed. The results showed that the toxicity of the 17 SFCCCs differ greatly. Ji SFCCC was the most toxic to R. subcapitata with a 96 h EC50 value of 1.38%, while Ha SFCCC was the least toxic, with the EC50 value was >100%. The relationships between the toxicity of SFCCCs and the metal concentrations in leachates were analyzed. The concentration of Ni (p = 0.001), La (p = 0.001), Mn (p = 0.014), Ce (p = 0.017), Co (p = 0.018), and Ca (p = 0.031) in leachates showed significant correlation with EC50 values. The predictive model for the ecotoxicity of SFCCCs were established with the concentrations of Ni and La in leachates as:ln(EC50) = 0.817 + exp(1.356 – 1.736 × CNi - 0.262 × CLa) (R2 = 0.926).The main toxic ingredients of SFCCC to microalgae were identified for the first time in this work. The results and predictive model of this study are significance for toxicity determination and management of SFCCCs.

Abstract Background Massive hemoptysis is a life-threatening condition. Massive hemoptysis caused by pulmonary vein stenosis (PVS) after radiofrequency catheter ablation for atrial fibrillation (AF) is rare. However, bilateral lung hemorrhage following bilateral PVS is extremely rare. Case presentation We herein describe a 62-year-old man with refractory massive hemoptysis after radiofrequency catheter ablation for AF, which was successfully controlled by surgical lobectomy and endovascular bilateral PV stenting. The hemorrhage was derived from the bilateral lungs following PV obstruction and bilateral PVS, which was definitively diagnosed by bronchoscopic examination. The patient had no recurrence of hemoptysis during a follow-up period of 30 months, and the PV stents had not narrowed as shown by computed tomography 30 months after stent placement. Conclusions Massive hemoptysis can be caused by bilateral PVS after radiofrequency catheter ablation for AF, and hemorrhage from the bilateral lungs in such patients is extremely rare. Nevertheless, cardiologists, interventional radiologists, and pulmonologists should consider the potential for massive hemoptysis caused by PVS.

Objective: The objective was to study the effect of total flavonoids of Herba Taxilli (TFHT) on osteoporotic (OP) rats induced by retinoic acid (RA) and explore its mechanism. Methods: Three-month-old female Sprague Dawley rats were randomly divided into six groups, each with ten rats, i.e. normal control group, model group, and positive control group and three TFHT groups (at high-, middle-, and low-doses). Except the normal control group, rats of all the other groups were administered RA by gavage (70 mg/kg/day) every morning for 14 days to establish an OP model; at the same time, all the rats were given Corresponding therapeutic drug by gavage for 28 days: rats in the three TFHT groups were given TFHT (400, 200, and 100 mg/kg/day, respectively) and rats in the positive control group were given Long mu Zhuang gu Granule 300 mg/kg/day. Rats in the model group and normal group were given an equal volume of 1% sodium carboxymethyl cellulose sodium suspension. After the experiment, blood was taken to measure the levels of serum calcium (Ca2+), alkaline phosphatase (ALP), and tartrate-resistant acid phosphatase (TRAP); the spleen, uterus, and ovary were dissected to determine the organ coefficient; the left hind limb femur was dissected to determine bone mineral density (BMD), bone dry weight (BDW), bone wet weight (BWW), bone diameter (BD), bone length (BL), bone dry weight/rat weight (BDW/W), bone wet weight/rat weight (BWW/W), bone dry weight/bone wet weight (BDW/BWW), bone dry weight/bone length (BDW/L), and bone wet weight/bone diameter (BWW/D); and the right hind limb femur was dissected to observe the bone path morphological changes. Results: Compared with the model group, the serum Ca2+ content, uterus plus ovary coefficient, BMD, BDW, BDW/W, BWW/W, BDW/BWW, BDW/L, and BWW/D in the TFHT groups increased, whereas the spleen coefficient, ALP, and TRAP decreased in the TFHT groups, and the differences were statistically significant (P < 0.05 orP < 0.01). Conclusion: TFHT was found effective in treating OP in rats induced by RA; the main mechanisms were increasing the serum Ca2+ content and reducing the level of ALP and TRAP.

Abstract Autophagy is a genetically well-controlled cellular process that is tightly controlled by a set of core genes, including the family of autophagy-related genes (ATG). Autophagy is a “double-edged sword” in tumors. It can promote or suppress tumor development, which depends on the cell and tissue types and the stages of tumor. At present, tumor immunotherapy is a promising treatment strategy against tumors. Recent studies have shown that autophagy significantly controls immune responses by modulating the functions of immune cells and the production of cytokines. Conversely, some cytokines and immune cells have a great effect on the function of autophagy. Therapies aiming at autophagy to enhance the immune responses and anti-tumor effects of immunotherapy have become the prospective strategy, with enhanced antigen presentation and higher sensitivity to CTLs. However, the induction of autophagy may also benefit tumor cells escape from immune surveillance and result in intrinsic resistance against anti-tumor immunotherapy. Increasing studies have proven the optimal use of either ATG inducers or inhibitors can restrain tumor growth and progression by enhancing anti-tumor immune responses and overcoming the anti-tumor immune resistance in combination with several immunotherapeutic strategies, indicating that induction or inhibition of autophagy might show us a prospective therapeutic strategy when combined with immunotherapy. In this article, the possible mechanisms of autophagy regulating immune system, and the potential applications of autophagy in tumor immunotherapy will be discussed.

This study aims to symmetrically improve the economy and environmental protection of combined cooling, heating and power microgrid. Hence, the characteristics of configuration ways of energy storage devices in traditional combined cooling, heating and power systems are analyzed, and a scheme for the operator to establish an energy storage station is designed. An improved aquila optimizer for the optimal configuration of the system is proposed to symmetrically enhance the economic and environmental protection performance. The feasibility of the proposed scheme is verified through experiments in three different places. The results show that the economic cost and exhaust emission of the system with energy storage station are reduced to varying degrees compared with the system with energy storage equipment alone and the system without energy storage equipment based on symmetry concept. Especially in Place 1, the scheme with energy storage station in the system can reduce the electric energy purchased from power grid by 43.29% and 61.09%, respectively, compared with other schemes. This study is conducive to promoting the development of clean energy, alleviating the energy crisis, reducing the power supply pressure of power grid, and improving the profits of operators by symmetrically considering the economic and environmental performance of the system.

Lithium battery is a new energy equipment. Because of its long service life and high energy density, it is widely used in various industries. However, as the number of uses increases, the life of the energy battery gradually decreases. Aging of battery will bring security risks to energy storage system. Through the life prediction of energy lithium battery, the health status of energy battery is assessed, so as to improve the safety of energy storage system. Therefore, a hybrid model is proposed to predict the life of the energy lithium battery. The lithium-ion battery capacity data are always divided into two scales, which are predicted by extreme learning machine and support vector machine model. The energy lithium-ion battery capacity attenuation data were obtained through experiments. The original signal is decomposed into five layers by using the wavelet basis function to denoise the signal. Finally, the denoised signal is synthesized. The noise reduction effect of each wavelet was analyzed. The analysis results show that the mean square error value of the Haar wavelet is 5.31e-28, which indicates that the Haar wavelet has the best noise reduction effect. Finally, the combined model was tested by using two sets of experiments. The prediction results of the combined model are compared with those of the single model. The test results show that the prediction results of the combined model are better than the single model for either experiment 1 or experiment 2. Experiment 1 indicated the root mean square error values are 29.58 and 79.68% smaller than the root mean square error values of extreme learning machine and support vector machine. The model proposed in this study has positive significance for the safety improvement of energy storage system and can promote the development and utilization of energy resources.

Through the accurate prediction of power load, the start and stop of generating units in the power grid can be arranged economically and reasonably. The safety and stability of power grid operation can be maintained. First, chicken swarm optimizer based on nonlinear dynamic convergence factor (NCSO) optimizer is proposed based on chicken swarm optimizer (CSO) optimizer. In NCSO optimizer, nonlinear dynamic inertia weight and levy mutation strategy are introduced. Compared with CSO optimizer, the convergence speed and effect of NCSO optimizer are obviously improved. Second, the random parameters of extreme learning machine (ELM) model are optimized by NCSO optimizer, and NCSOELM model is established to predict the power load. Finally, the NCSO optimization extreme learning machine (NCSOELM) model is used to predict the power load, and compared with back propagation (BP), support vector machine (SVM) and CSO optimization extreme learning machine (CSOELM) model. The experimental results show that the fitting accuracy of NCSOELM model is high, and the determination coefficient r 2 is above 90%. And the root mean square error value of the NCSOELM model is 0.87, 0.41, and 0.25 smaller than the root mean square error values of the support vector machine, BP, and CSOELM models, respectively. Experiments show that the model proposed in this study has high fitting effect and low prediction error, which is of positive significance for the realization of economic and safe operation of energy system.

Photovoltaic microgrids provide free renewable energy solutions for Rwandans. Although solar technology keeps on its advancement, hydropower remains the principal power source in Rwanda. Other renewable power sources include wind and geothermal energies that are not yet fully exploited. Nonrenewable sources in Rwanda including methane, peat, thermal, and fuels are also used for providing energy solutions for the citizens. Rwanda Energy Group (REG) sets the energy strategic plan since 2015 for achieving the minimum of 512 MW of energy production in 2024/2025 to meet the total energy demand. The plan predicted 52% for grid-connected and 48% for off-grid (standalone) connections. The literature survey and data analysis collected on site were used to evaluate and determine the best cheaper microgrid model from the three comparison case studies for the household in Rwanda. The study focused on the economic power generation model mainly based on solar resources to minimize the electricity cost and provide income for the excess energy produced. Moreover, the study resulted in a low-cost (four times cheaper), reliable, and affordable grid-connected PV and battery microgrid model for a residential home with a minimum daily load of 5.467 kWh. The simulation results based on economic comparison analysis found the levelized cost of energy (LCOE) and net present cost (NPC) for each power-generated model by using Hybrid Optimization Model for Electric Renewable (Homer) pro software. The results show that the LCOE for electricity production by each of the Grid connected-PV-Battery system, Diesel GenSet-PV-Batteries, and PV-Batteries systems was 0.0645 US$/1 kWh, 1.38 US$/1 kWh and 1.82 US$/1 kWh, respectively, compared with 0.2621 US$/1 kWh, the current residential electricity price (2020) for Rwanda.

Objective: This study aimed to investigate effective components of dried ginger (DG) in warming lung to reduce watery phlegm and in vivo tissue distribution on the syndrome of cold fluid retained in lung of rats with chronic obstructive pulmonary disease (COPD) by means of the “syndrome-efficacy-biological sample analysis” method and then to explore its meridian tropism. Methods: Wistar rats were given nasal drops of 200-μL lipopolysaccharide and smoke 30 min two times a day, then put the appropriate dose of ice water, and freeze for an hour to build model rats. On the 16th day, the drug group was orally administered of DG (500 mg/mL) until the 30th day. Blood samples and biological tissues were collected from the orbital venous plexus into heparinized hemostasis tubes at 5, 10, 15, 30, 45, 60, 90, 120, 180, 240, and 360 min after the last administration. Using ultraviolet-high-performance liquid chromatography (Waters, USA) method, the effective components were tested, and DAS 3.0 software (Mathematical Pharmacology Professional Committee of China, Shanghai, China) was used to analyze the results. Results: The compounds of DG entering into blood were 6-gingerol, 6-shogaol, and 8-gingerol. Tissue distribution analysis indicates that three active ingredients are widely present in the lung, spleen, kidney, liver, heart, large intestine, stomach, small intestine, and other organs of rats with COPD. Conclusions: 6-gingerol, 6-shogaol, and 8-gingerol belong to effective components of DG in curing the syndrome of cold fluid retained in lung of rats with COPD and mainly distributed in organs including the spleen, stomach, lung, kidney, liver, and heart.

The blue fox (Vulpes lagopus), a fur-bearing animal, is an important component of the breeding industry in China. Semen quality is a key factor for the reproductive process and the breeding effectiveness of the farmed blue fox. However, bacterial contamination in semen samples utilized in artificial fertilization is very common. The β-defensins, a class of important antimicrobial peptides in mammals, could protect the reproductive system of male animals from bacterial invasion, maintain the stability of the genital tract microenvironment and improve semen quality. In this study, molecular cloning and bioinformatics analysis were conducted to analyze the protein structure and function of blue fox β-defensin 108 (Vulpes lagopus beta-defensin 108, vBD108) and 122 (Vulpes lagopus beta-defensin 122, vBD122). To evaluate the bacteriostatic effect of recombinant vBDs (Vulpes lagopus beta-defensins) protein, varying concentrations (0, 25, 50, 100, 200 µg/mL) were taken to evaluate the effects on Escherichia coli and Staphylococcus aureus at different times (0, 2, 4, 6, 8, 12 h). The results showed that vBD108 and vBD122 existed in different forms in protein structure and had antibacterial activity. Both proteins, at 50 µg/mL, had efficacious bacteriostatic activity. This study shows that recombinant vBD108 and vBD122 proteins have good antibacterial activity in vitro. This implies a potential role in improving semen quality and hygienic measures in the process of artificial insemination as an extender of semen dilution with antibacterial activity.

In this study, small molecular (≤3 kDa) Coix glutelin hydrolysate (CGH) was obtained by the method of enzymatic hydrolysis and then immunomodulatory activity was evaluated in vitro and in vivo. Stimulation index of mice splenocytes was increased in a dose-dependent manner after treated with 3.125–25 μg/mL CGH. Acid phosphatase in mice peritoneal macrophages was significantly improved after stimulated with CGH at some concentration. The CGH stimulated the secretion of NO from Raw264.7 in a concentration-dependent manner and also significantly inhibited lipopolysaccharide-induced cell excessive activation at some extent. After successive administration of CGH for 21 days, the spleen index of ICR was significantly increased to 3.73 mg/g for the group administrated 800 mg/kg CGH. Malondialdehyde (MDA) assay displayed that 200 mg/kg CGH increased mice serum MDA concentration, while the variation was obsolete in the other CGH groups. All results demonstrated that CGH took essential effects on immunomodulatory activities.

The purpose of our study was to investigate the underlying mechanism and functional role of microRNA-145 (miR-145) in cervical cancer. In this study, quantitative real-time PCR (qRT-PCR) was used to detect miR-145 and FSCN1 expression levels in tissues and HeLa cells. Western blotting was performed to determine the protein level of FSCN1. The luciferase assay was used to verify the direct target of miR-145. The CCK-8 assay and 2D colony formation assays were performed to determine the effects of miR-145 mimics or FSCN1 silencing on cell proliferation. miR-145 expression levels were significantly down-regulated, while FSCN1 expression levels were significantly up-regulated in the cervical carcinoma tissues compared with their matched non-cancerous tissues. In addition, FSCN1 expression levels were negatively correlated to miR-145 in tissues. Next, FSCN1 was verified as the direct target of miR-145 in HeLa cells. Moreover, overexpression of miR-145 dramatically inhibited the proliferation of HeLa cells. The silencing of FSCN1 exhibited the similar patterns on cell proliferation as miR-145 overexpression. The miR-145/ FSCN1 axis contributes to the progression of cervical cancer by inhibition of cervical cancer cell proliferation.

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

With the development of reliability theory, people realized that “absolutely reliable” machines could not be made. With its incomparable advantages, the high-speed permanent-magnet brushless DC motor is usually used in the symmetrical structure of high-speed operation working systems, which at present are widely used in aerospace and other fields. The structure of the manufacturing process involves a strict processing, but in the process of work failure could still occur. No matter what field the high-speed permanent magnet brushless DC motor is applied to, it is very important to identify states and run fault diagnosis, which is of great significance to maintain the reliability of the motor and its working system. In this study, the fault diagnosis method of a high-speed permanent-magnet brushless DC motor is studied, and a combination model of modified gray wolf optimization algorithm (MGWO) and support vector machine (SVM) have been proposed for the motor fault diagnosis research. Based on the traditional gray wolf optimization (GWO) algorithm, the optimization performance of the algorithm is improved by initializing the population through a tent map and introducing a sine wave dynamic adaptive factor. Then the modified algorithm is used to optimize the internal parameters of SVM to improve the diagnostic accuracy of the model. Through the signal acquisition test, the current signals under different fault states and faultless states were collected, and the current signal data set required for the experiment is obtained. The experimental result showed that, compared with GWO or sailfish optimization (SFO) optimized SVM models, Extreme learning machine and Back Propagation neural network classical classification models, the fault diagnosis accuracy of the proposed model is the highest, proving the excellent classification performance and good robustness of the MGWO-SVM model.

This study extracted the featured vectors in the same way from testing data and substituted these vectors into a trained hidden Markov model to get the log likelihood probability. The log likelihood probability was matched with the time–probability curve from where the gyro motor state evaluation and prediction were realized. A core component of gyroscopes is linked to the reliability of the inertia system to conduct gyro motor state evaluation and prediction. This study features the vectors’ extraction from full life cycle gyro motor data and completes the training model to feature the vectors according to the time sequence and extraction to full life cycle data undergoing hidden Markov model training. This proposed model applies to full life cycle gyro motor data for validation, compared with traditional hidden Markov model predictive methods and health condition-trained data. The results suggest precise evaluation and prediction and provide an important basis for gyro motor repair and replacement strategies.

The complexity of the loads acting on the offshore wind turbines (OWTs) structures and the significance of investigation on structure dynamics are explained. Test results obtained from a scaled wind turbine model are also summarized. The model is supported on monopile, subjected to different types of dynamic loading using an innovative out of balance mass system to apply cyclic/dynamic loads. The test results show the natural frequency of the wind turbine structure increases with the number of cycles, but with a reduced rate of increase with the accumulation of soil strain level. The change is found to be dependent on the shear strain level in the soil next to the pile which matches with the expectations from the element tests of the soil. The test results were plotted in a non-dimensional manner in order to be scaled to predict the prototype consequences using element tests of a soil using resonant column apparatus.

For a nonlinear limit state function, the first-order reliability method (FORM) may cause large errors in the computation of not only the reliability index or failure probability but also the reliability sensitivity. In order to obtain more the accurate results of the reliability sensitivity analysis, a number of hyperplanes are built near the design point by first-order Tayler series expansion, which replace the known nonlinear limit state hypersurface, and an equivalent computational method is utilized to construct an equivalent hyperplane of the obtained hyperplanes. And the reliability sensitivities can be estimated more accurately by the derived equations based on the equivalent hyperplane. An example shows that the method is effective and feasible.

Photovoltaic power is now a major green energy resource, and its generated power can be directly connected to the power grid. However, the stability of power grid may be affected by the random and intermittent characteristics of photovoltaic power. In order to solve this problem, a forecasting model based on the deep belief nets is proposed. First, affecting factors of photovoltaic power generation are studied, including solar radiation intensity, air temperature, relative humidity, and wind speed. Based on the correlation coefficient between output power and each factor, the most influential factors can be determined and used as inputs of the proposed forecasting model for training process. Second, the forecasting model is then established and applied to predict the photovoltaic output powers for 2 weeks in summer and winter, respectively. The mean absolute percentage error, mean squared error, and Theil’s inequality coefficient are used to evaluate the performance efficiency between the proposed deep belief net model and back propagation neural network model. The performance outcomes reveal that the proposed deep belief net model can improve the prediction errors with rapid convergence significantly, better than the back propagation model.

Laboratory aerobic incubation was conducted for 161 d to study N mineralization and the changes of organic N fractions of nine different manures (3 chicken manures, 3 pig manures and 3 cattle manures) from different farms/locations. Results indicated that significant (P

In photovoltaic and wind power storage systems, the reliability of the battery directly affects the overall reliability of the energy storage system. Failed batteries can seriously affect the stable operation of energy storage systems. This paper aims to improve the reliability of the storage systems by accurately predicting battery life and identifying failing batteries in time. The current prediction models mainly use artificial neural networks, Gaussian process regression and hybrid models. Although these models can achieve high prediction accuracy, the computational cost is high due to model complexity. Least squares support vector machine (LSSVM) is a computationally efficient alternative. Hence, this study combines the improved chicken swarm optimization algorithm (ICSO) and LSSVM into a hybrid ICSO-LSSVM model for the reliability of photovoltaic and wind power storage systems. The following are the contributions of this work. First, the optimal penalty parameter and kernel width are determined. Second, the chicken swarm optimization algorithm (CSO) is improved by introducing chaotic search behavior in the hen and an adaptive learning factor in the chicks. The performance of the ICSO algorithm is shown to be better than CSO using standard test problems. Third, the prediction accuracy of the three models is compared. For NMC1 battery, the predicted relative error of ICSO-LSSVM is 0.94%; for NMC2 battery, the relative error of ICSO-LSSVM is 1%. These findings show that the proposed model is suitable for predicting the failure of batteries in energy storage systems, which can improve preventive and predictive maintenance of such systems.

Gingerols and shogaols are recognized as active ingredients in ginger and exhibit diverse pharmacological activities. The preclinical pharmacokinetics and tissue distribution investigations of gingerols and shogaols in rats remain less explored, especially for the simultaneous analysis of multi-components. In this study, a rapid, sensitive, selective, and reliable method using an Ultra-Performance Liquid Chromatography Q-Exactive High-Resolution Mass Spectrometer (UPLC-Q-Exactive⁻HRMS) was established and validated for simultaneous determination of eight compounds, including 6-gingerol, 6-shogaol, 8-gingerol, 8-shogaol, 10-gingerol, 10-shogaol, Zingerone, and 6-isodehydrogingenone in plasma and tissues of rats. The analytes were separated on a Syncronis C18 column (100 × 2.1 mm, 1.7 µm) using a gradient elution of acetonitrile and 0.1% formic acid in water at a flow rate of 0.25 mL/min at 30 °C. The method was linear for each ingredient over the investigated range with all correlation coefficients greater than 0.9910. The lowest Lower Limit of quantitation (LLOQ) was 1.0 ng/mL. The intra- and inter-day precisions (Relative Standard Deviation, RSD%) were less than 12.2% and the accuracy (relative error, RE%) ranged from −8.7% to 8.7%. Extraction recovery was 91.4⁻107.4% and the matrix effect was 86.3⁻113.4%. The validated method was successfully applied to investigate the pharmacokinetics and tissue distribution of eight components after oral administration of ginger extract to rats. These results provide useful information about the pharmacokinetics and biodistribution of the multi-component bioactive ingredients of ginger in rats and will contribute to clinical practice and the evaluation of the safety of a Chinese herbal medicine.

Using a cell-based cytotoxicity assay three new cytotoxic azaphilones, including two stereoisomers and designated monapurones A-C (1-3), were isolated from the extract of Monascus purpureus-fermented rice (red yeast rice). Their structures were elucidated by detailed interpretation of spectroscopic and chemical data. The relative configurations were assigned on the basis of analysis of NOE data, and the absolute configurations were determined by direct comparison of their CD spectra with those of known azaphilones and chemical correlations. In the in vitro assays, monapurones A-C (1-3) showed selective cytotoxicity against human cancer cell line A549 with IC50 values of 3.8, 2.8 and 2.4mM respectively, while exhibiting no significant toxicity to normal MRC-5 and WI-38 cells at the same concentration.

In order to solve the problems caused by the susceptibility to changing weather conditions and the complex load conditions of photovoltaic (PV) systems, and the fact a single target inverter control strategy cannot effectively mitigate large voltage fluctuations at point of common coupling (PCC), an integrated reactive power control strategy for PV inverters is proposed. According to the weather and load conditions, this strategy is divided into four control modes: normal operation control mode, reverse power control mode, cloudy control mode and night control mode. The four control modes switch between each other under the specific switching rules to ensure an appropriate quantity of reactive power injection or consumption in the PV inverter. The effects of the four control modes in isolated operation were significant. The deviation rate of PCC voltage from its standard value was mitigated at about 2% under the integrated control strategy. Therefore, the proposed control strategy has a high application value in improving power quality and maximizing utilization of PV inverters.

In response to the issue of short-term fluctuations in photovoltaic (PV) output due to Cloud movement, this paper proposes a method for forecasting short-term PV output based on a Depthwise Separable Convolution Visual Geometry Group (DSCVGG) and a Deep Gate Recurrent Neural Network (DGN). Initially, a cloud motion prediction model is constructed using a DSCVGG, which achieves edge recognition and motion prediction of clouds by replacing the previous convolution layer of the pooling layer in VGG with a depthwise separable convolution. Subsequently, the output results of the DSCVGG network, along with historical PV output data, are introduced into a Deep Gate Recurrent Unit Network (DGN) to establish a PV output prediction model, thereby achieving precise prediction of PV output. Through experiments on actual data, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) of our model are only 2.18% and 5.32 x 10-5, respectively, which validates the effectiveness, accuracy, and superiority of the proposed method. This provides new insights and methods for improving the stability of PV power generation. [ABSTRACT FROM AUTHOR]

The wake effect caused by a wind turbine can reduce the wind speed and add turbulence to the wind, thus impacting the power generation efficiency. To effectively enhance the generated power in wind farms, we propose an optimal layout model that is combined with artificial intelligence optimizing algorithms. First, the adaptive genetic algorithm (AGA) is used to optimize the collected wind speed and direction distribution data as the optimization basis. Second, the extreme learning machine (ELM) based on Monte Carlo simulation is used to establish a guidance for determining the turbine relocation from the optimization basis. Simultaneously, the dung beetle optimization (DBO) algorithm is developed to improve the performance of ELM for achieving the optimal solution. The proposed model was tested at six different wind farms and under three different wind condition distribution settings. The simulation results verify that the model is superior to existing algorithms in reducing the wakeeffect impact as well as optimizing the wind farm layout. [ABSTRACT FROM AUTHOR]

The integrated energy system is a perfect way to realize the transformation of the traditional energy industry structure. To further explore the role of its load-side adjustable potential in carbon emission reduction, an optimal operation model of the integrated energy system considering the carbon emission sensing measurement system and demand response (DR) is proposed. First, the integrated electricity--heat energy system (IEHS) model framework is constructed in accordance with the coupling characteristics of electricity--heat--gas in the system. The carbon emission sensing measurement system is introduced on the energy supply side, and DR is considered on the user load side, including the DR model based on the price elasticity matrix and the replacement-based DR model considering the mutual conversion of electric and thermal energies on the energy use side. Second, the baseline method is used to allocate carbon emission quotas for the system free of charge, and the actual carbon emissions of gas turbines and gas boilers are considered. An IEHS objective function is established to minimize the sum of the energy purchase, carbon transaction, and operation and maintenance costs. Third, an improved northern goshawk optimization (INGO) algorithm is proposed to optimize the low-carbon operation of the IEHS model. Finally, the effectiveness and practicability of the proposed model and algorithm are verified using different scenarios and different algorithms. The results show that, considering the carbon emission sensing measurement system and DR, the total operation cost is reduced by 10.4% and the actual carbon emission is reduced by 6420.582 kg. Compared with those of the northern goshawk (NGO) algorithm, the total operation cost of the INGO algorithm is reduced by 9.4% and the actual carbon emission is reduced by 1164.253 kg, which realizes the coordinated operation of system economy and low carbon emission. [ABSTRACT FROM AUTHOR]