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Abstract To further raise the gas extraction efficiency of the tight sandstone, the liquid nitrogen (LN2) freezing-thawing cycles method can be employed to improve the permeability of the low-permeability reservoirs. Permeability is generally regarded as a macroscopic description of the pore structure and usually has functional relationship to pore structure properties. The permeability of the rock is closely related to the change of microscopic pore structure. The permeability of rock depends on how the subzero temperatures changed the microscopic pore structure of rock, but it has not yet been confirmed obviously. In this study, the nuclear magnetic resonance (NMR) technique was adopted to investigate the pore structure evolution law and permeability of the tight sandstone with different LN2 freezing-thawing cycles. The results demonstrate that the LN2 freezing-thawing cycles promotes pore development and micro-fracture connection, and enhances the pore connectivity. The proportion of meso-pores, macro-pores and micro-fractures in the sandstone samples increases significantly, which provides a channel for the sandstone gas flow and extraction. Total porosity and effective porosity of the samples present a trend of rapid increase as the number of LN2 freezing-thawing cycles increasing, while the residual porosity decreases as the number of LN2 freezing-thawing cycles increasing. Coates model, SDR model (mean T2 model) and PP model were used to calculate and evaluate the permeability of the samples subjected to different LN2 freezing-thawing cycles. Furthermore, PP model can provide a better permeability estimate than the classical Coates and SDR model.

Abstract Demyelination occurs widely in the central nervous system (CNS) neurodegenerative diseases, especially the multiple sclerosis (MS), which with a complex and inflammatory lesion microenvironment inhibiting remyelination. Sirtuin6 (SIRT6), a histone/protein deacetylase is of interest for its promising effect in transcriptional regulation, cell cycling, inflammation, metabolism and longevity. Here we show that SIRT6 participates in the remyelination process in mice subjected to LPC-induced demyelination. Using pharmacological SIRT6 inhibitor or activator, we found that SIRT6 modulated LPC-induced damage in motor or cognitive function. Inhibition of SIRT6 impaired myelin regeneration, exacerbated neurological deficits, and decreased oligodendrocyte precursor cells (OPCs) proliferation and differentiation, whereas activation of SIRT6 reversed behavioral performance in mice, demonstrating a beneficial effect of SIRT6. Importantly, based on RNA sequencing analysis of the corpus callosum tissues, it was further revealed that SIRT6 took charge in regulation of glial activation during remyelination, and significant alterations in CHI3L1 were obtained, a glycoprotein specifically secreted by astrocytes. Impaired proliferation and differentiation of OPCs could be induced in vitro using supernatants from reactive astrocyte, especially when SIRT6 was inhibited. Mechanistically, SIRT6 regulates the secretion of CHI3L1 from reactive astrocytes by histone-H3-lysine-9 acetylation (H3K9Ac). Adeno-associated virus-overexpression of SIRT6 (AAV-SIRT6-OE) in astrocytes improved remyelination and functional recovery after LPC-induced demyelination, whereas together with AAV-CHI3L1-OE inhibits this therapeutic effect. Collectively, our data elucidate the role of SIRT6 in remyelination and further reveal astrocytic SIRT6/CHI3L1 as the key regulator for improving the remyelination environment, which may be a potential target for MS therapy.

Abstract The aim of this study was to evaluate the effects of medroxyprogesterone acetate (MPA) treatment in comparison to those of gonadotropin releasing hormone (GnRH) antagonists for the prevention of premature luteinizing hormone surges during controlled ovarian hyperstimulation (OS) and the impact of these effects on developing embryos and pregnancy outcomes. Data from 757 cycles of GnRH antagonist treatment and 756 cycles of MPA treatment were evaluated at the Akdeniz University Faculty of Medicine Assisted Reproductive Treatment Center between October 2018 and April 2022. Patient records were obtained from the electronic database of the centre and analysed. In our centre, GnRH antagonist protocols were used between 2018 and 2020, and MPA protocols were used between 2020 and 2022. We chose our study population by year. Our study is a comparative retrospective study. All methods in this study were performed in accordance with the relevant guidelines and regulations. Patients using MPA were significantly older (33.9 ± 5.6 vs. 32.6 ± 5.6, p

Abstract The biaxial compression experiments of rock-like materials with two flaws are carried out under different flaw inclination angle, rock bridge ligament angle, lateral stress. The experimental studies show that crack propagation modes of rock-like material are as follows: wing crack through mode (Y mode), shear crack through mode (J mode), mixed crack through mode (wing shear JY mode), longitudinal extension of crack and transverse shear splitting. prefabricated fractured rock specimens have experienced the closing stage of prefabricated fractures, the elastic deformation stage, the generation and expansion of cracks (or plastic strengthening), and the residual loading stage. The peak strength of the specimen is increases with the increase of flaw inclination angle and lateral stress. With the increase of the rock bridge ligament angle, the failure of the rock bridge region changes from the shear crack failure to composite failure of shear crack and the wing type tensile crack failure, and then to the wing crack failure. With the increase of the lateral pressure, the failure of the specimen changes from the wing type tensile crack failure to the wing type and shear crack failure, and then to shear crack failure. The flaw inclination angle mainly changes the form of crack growth but does not effect on the failure modes. The counting number of acoustic emission events at the center of the sample is relative large, indicating that the cacks appear in the part of the rock bridge firstly. With the increasing of loads, the cracks of the rock bridge expanding constantly and connecting finally. The changes of acoustic emission event counts is consistent with the macroscopic damage form obtained from the experiments.

Abstract To solve the problem of oscillation instability in permanent magnetic synchronous generator (PMSG)-based wind power connected systems during low-voltage ride through (LVRT) process, a parameter adjustment strategy based on interaction energy path optimization is proposed in this paper. Firstly, a modular state-space model of PMSG under fault transient conditions is constructed, and the system is divided into five subsystems. Then, the dynamic energy function of subsystems reflecting the oscillation stability of the system is derived. Based on that, the dynamic energy flow path is described considering the introduction of LVRT control. On this basis, the interaction energy between LVRT control links and subsystems is analyzed, and the coupling mechanism of voltage support and damping characteristics in the LVRT process is explained. Further, aiming at the optimal change rate of the total interaction energy in the LVRT process, the adjustment strategy of LVRT control parameters is constructed to meet voltage and damping requirements. Finally, a PMSG-connected system model is built on the MATLAB/Simulink platform to verify the effectiveness of the adjustment strategy. The results show that the proposed method can effectively improve the damping level under the fault transient condition, as well as supporting system voltage.

Abstract Background Successive stamen movement is a complex plant behavior involving successive uplift of stamens and pollen release, which plays a role in reducing sexual interference, increasing pollen deposition and promoting pollen export. Although reported from several taxa, studies on whether the movement can be influenced by abiotic and biotic factors are scarce. Methods In this study, we here for the first time described a pattern of successive stamen movement in Saxifraga candelabrum (Saxifragaceae). We then compared the rates of stamen movement in S. candelabrum under different weather and varying pollinator visits. Pollen packaging and presentation schedule of S. candelabrum were also investigated. Results The results showed that the number of stamens bent per day in sunny days was significantly higher than overcast and rain. Flowers that receive more pollinator visits (control treatment) had significantly higher number of stamen movement than those that received fewer (removal treatment) and none (bagging treatment). Throughout the staminate phase of a flower, there was a progressive increase in both pollen quantity of individual stamens and pollen presentation during each day. Conclusion Our research demonstrates that successive stamen movement in S. candelabrum was accelerated by favorable weather and increased pollinator visits, which may promote pollen export. Moreover, incremental pollen packaging is likely an adaptation to seasonal regularity in variations of sex ratio resulting from protandry.

The role of load modeling in power systems is crucial for both operational and regulatory considerations. It is essential to develop an effective and reliable method for optimizing load modeling parameter identification. In this paper, the dung beetle algorithm is improved by using the good point set, and a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (GDBO) within the framework of the measurement-based load modeling method. The proposed parameter identification strategy involves utilizing PMU voltage data as input, selecting a comprehensive load model, and refining the initialization process based on the good point set to mitigate the influence of local maxima. Through iterative optimization of the objective function using the Dung Beetle Optimizer (DBO) algorithm, the optimal parameters for the comprehensive load model are determined, enhancing the model’s ability to accurately capture the power curve. Analysis of examples pertaining to PMU-measured modeling parameter identification reveals that the proposed GDBO algorithm, which incorporates a good point set, outperforms alternative methods such as the improved differential evolution algorithm (IDE), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO), and conventional DBO algorithm. This demonstrates the superior performance of the introduced approach in the context of load model parameter identification.

Faults in three-core armored cable lines in distribution networks are frequent, and using only zero-sequence current time-frequency data to identify fault lines has its limitations. To increase the accuracy of cable fault identification, this paper utilizes the cable ground wire current and proposes a new fault line identification method that integrates a Bidirectional Gated Recurrent Unit (BiGRU), a Residual Network (ResNet), and a Multi-head Attention Mechanism (MA). Firstly, the Northern Goshawk Algorithm optimizes Variational Mode Decomposition (NGO-VMD) for denoising the ground wire current signal, reducing interference from the complex external environment. Secondly, the processed current signal is used to generate two-dimensional images using the Markov Transition Field (MTF), expanding signal features. Finally, BiGRU-ResNet-MA is utilized to deeply extract, enhance, and identify the multi-dimensional features of the ground wire current signal. The BiGRU-ResNet-MA model combines the two-dimensional image spatial characteristic extraction capability of ResNet with the temporal characteristic extraction capability of BiGRU, and further enhances feature weighting through MA, improving the fault line identification capability. The simulation findings show that this identification method can effectively address the issue of traditional methods relying too heavily on subjective selection. It accurately identifies fault lines even in the presence of strong noise, unstable faults, and asynchronous sampling, showcasing its strong robustness.

In this paper, a single-antenna full-duplex subsystem is proposed, consisting of a high isolation network and a stacked patch antenna with reflector. The employed patch antenna is fed by two ports with very similar input impedances to make the reflected signals identical. The high isolation network composed of two hybrids and two circulators plays a crucial part in achieving high transmitting to receiving (Tx-Rx) isolation. It is able to cancel out the inevitable reflected signals from the antenna ports and the leakage signals from the circulators. The theoretical analysis is presented and the subsystem is also fabricated and measured. According to the measurement results, across the operation band from 2.018 to 2.12 GHz, the subsystem has VSWR 50 dB, axial ratio 10.2 dBic. Compared with the state-of-art single-antenna full-duplex subsystems, the proposed design features high Tx-Rx isolation level and high gain, which is suitable for microwave radio relay communication and satellite detection application.

Phospholipase C zeta (PLCζ) is a key sperm-borne oocyte-activating factor that triggers Ca2+ oscillations and the subsequent block to polyspermy following gamete fusion. Mutations in PLCZ1, the gene encoding PLCζ, cause male infertility and intracytoplasmic sperm injection (ICSI) fertilization failure; and PLCζ expression and localization patterns are significantly correlated with ICSI fertilization rate (FR). However, in conventional in vitro fertilization (cIVF), whether and how sperm PLCζ affects fertilization remain unclear. Herein, we identified one previously reported and two novel PLCZ1 mutations associated with polyspermy in vitro that are characterized by excessive sperm–zona binding and a delay in pronuclei (PN) formation. Immunofluorescence staining and oocyte activation testing revealed that virtually all spermatozoa from patients lacked functional PLCζ and were thus unable to evoke Ca2+ oscillations. ICSI with an artificial oocyte activation treatment successfully rescued the polyspermic phenotype and resulted in a live birth. Furthermore, we analyzed PLCζ in an additional 58 males after cIVF treatment in the Reproductive and Genetic Hospital of CITIC-Xiangya (Changsha, China) between February 2019 and January 2022. We found that the proportion of spermatozoa that expressed PLCζ was positively correlated with both 2PN rate and total FR. The optimal cutoff value below which males were likely to experience low FR (total FR ≤30%) after cIVF was 56.7% for the proportion of spermatozoa expressing PLCζ. Our study expands the mutation and the phenotypic spectrum of PLCZ1 and further suggests that PLCζ constitutes a promising biomarker for identifying low FRs cases in cIVF due to sperm-related oocyte activation deficiency and that sperm PLCζ analysis may benefit the wider male population and not only men with ICSI failure.

This study handles the specific aspects of oral communication at various linguistic dimensions within the context of teaching French as a foreign language. Oral language is prone to a particular organization at the prosodic, morphosyntactic, and discursive dimensions, which interact once speech is brought into play. In order to delve into orality in French, as the problematic of this study, and to draw its pedagogical implications, a linguistic review of some hypothetical utterances was first carried out. Then, an interview-based video retrieved from an open source was analyzed in terms of its specific oral aspects by excluding lexical and kinesic characteristics. The findings show the relevance of some oral features which bear a close relationship with each other. As far as the prosodic aspects are concerned, it is found that the filled pause, the false start, the contraction, the syllable lengthening, and most importantly the intonation, which plays a distinctive role when conveying meaning, are utilized in oral communication in French. The relevant morphosyntactic features of orality in the scope of French include the thematization, the omission of the first negative adverbial particle, the use of presentative structures, neutral demonstrative pronouns as well as that of bivalent indefinite/personal pronoun, and the recourse to direct speech or to various corrections/repetitions/reformulations. Concerning the specific discursive aspects of French, the findings reveal the use of phatic markers as pragmatic discourse organizers. Lastly, based on the findings, a communicative-linguistic activity in the form of a task was proposed for didactic purposes.

Earthquakes are natural events which can cause serious injuries and deaths in the affected areas. Orthopedic injuries are also very common in these events. The orthopedic surgeon’s role includes interventions such as the temporary or definitive treatment of fractures, debridement, amputation, and fasciotomy. In this review, open fractures, crush injuries, compartment syndrome, and patients with crush syndrome and fractures which develop without being under debris, which are frequently encountered by orthopedic surgeons after an earthquake, will be reviewed with case examples in light of the literature.

Introduction:Cardiopulmonary resuscitation (CPR) is a dynamic and memorizing process that is fully defined by guidelines. This process requiring urgent approach can only be achieved through repetitive training and practice. It is aimed to determine whether there are differences of knowledge between these trainings before and after, or whether the repeated training periodically increases the knowledge and skills of trained practitioners.Methods:CPR trainings were given to pediatric health and diseases assistant physicians by advanced cardiac life certified trainer. Pre-training, post-training and 6 months after the training, 20 questions were prepared based on the 2015 American Heart Association guide assessment questions were applied.Results:The most correct answers were given to the evaluation questions made immediately after the training (average number of correct answers 16.06±2.50) and the least correct answers were given to the evaluation questions made before training (average number of correct answers 8.41±2.26). Six months after the training, although the number of correct answers of the participants decreased significantly after the post-training; it was found that the scores in the sixth month were still considerably higher than before the training (average number of correct answers 12.76±3.30). There was a significant difference between the correct answers given to pre-training evaluation questions according to the duration of work in emergency, between physicians who have never worked yet and physicians with experience of working in the emergency department for 5 months and more (p=0.024). A significant difference was found between the first year assistant and the third year assistant (p=0.024) and between the first year assistant and the fourth year assistant (p=0.017) in terms of the number of correct answers to the pre-training evaluations questions.Conclusion:Theoretical and practical training in small groups increases the level of knowledge and skills about CPR.

Human activities, such as reservoir construction and riverbed sand extraction, significantly influence the hydrological and sedimentary dynamics of natural rivers, thereby directly or indirectly affecting river landscape pattern distribution. This study primarily focused on the lower Beijiang River (LBR) in China, an area characterized by intensive human activity. River landscape patterns were studied using historical topographical data and time-series Landsat remote sensing images. Natural and anthropogenic factors were considered to explore the driving forces behind the evolution of landscape patterns. The results indicated that the topography of the LBR underwent significant downcutting from 1998 to 2020. The average elevation of the study area decreased by 3.6 m, and the minimum thalweg elevation decreased by 6.7 m. Over the past 30 years, the local vegetation showed a relatively stable spatial distribution, whereas the area of sand remained relatively stable before 2012, followed by a sudden decline, and tended to stabilize in the last decade. The water area exhibited a gradually increasing trend. The transition maps indicated that the spatial changes in sand were the most significant, with only 39.6% of the sand remaining unchanged from 1998 to 2009 and 32.3% from 2009 to 2020. The corresponding landscape patterns showed that the fragmentation degree of sand increased, with the mean patch size decreasing by 69.2%. The aggregation of water intensified, as its aggregation index increased from 93.31% to 95.41%, while the aggregation of vegetation remained relatively minor, ranging from 89.52% to 90.12%. The annual average temperature, annual average maximum temperature, and annual rainfall days had the strongest correlations with the vegetation landscape pattern indices. Additionally, human activities may have been the primary driver of the landscape pattern evolution of water and sand. The findings of this study have positive implications for the maintenance of the diversity and stability of river ecosystems.

When a single-phase grounding fault occurs in resonant ground distribution network, the fault characteristics are weak and it is difficult to detect the fault line. Therefore, a fast fault line selection method based on MCECA-CloFormer is proposed in this paper. Firstly, zero-sequence current signals were converted into images using the moving average filter method and motif difference field to construct fault data set. Then, the ECA module was modified to MCECA (MultiCNN-ECA) so that it can accept data input from multiple measurement points. Secondly, the lightweight model CloFormer was used in the back end of MCECA module to further perceive the feature map and complete the establishment of the line selection model. Finally, the line selection model was trained, and the information such as model weight was saved. The simulation results demonstrated that the pre-trained MCECA-CloFormer achieved a line selection accuracy of over 98% under 10 dB noise, with a remarkably low single fault processing time of approximately 0.04 s. Moreover, it exhibited suitability for arc high-resistance grounding faults, data-missing cases, neutral-point ungrounded systems, and active distribution networks. In addition, the method was still valid when tested with actual field recording data.

With the goal of “carbon peak and carbon neutrality,” the need for environmentally friendly gases to replace SF6, a high greenhouse effect gas, is urgent. C4F7N, as an environmentally friendly gas with the greatest potential to replace SF6 as an arc extinguishing medium in circuit breakers, can be mixed with CO2 to greatly improve the shortcomings of its high liquefaction temperature, and the calculation of the physical parameters of the mixed gas plasma is a prerequisite for the computational simulation of the arc process in the opening of circuit breakers. Because solving the physical parameters is expensive, based on the system of differential equations, this paper adopts several machine learning algorithms by mining the relationship between the data using the known physical parameter data to predict the results of the physical parameters to be solved under certain conditions, which greatly reduces the cost of computation. The machine learning algorithms used in this paper are K-nearest-neighbor regression, decision tree, random forest, support vector machine, and gradient boosting regression, of which for the support vector machine, hyperparameters find it difficult to determine the problem of optimization using the gray wolf algorithm. The prediction results of several algorithms show that they are more accurate and that the problem can be solved better by using the method of machine learning. Finally, the comparison results show that the support vector machine exhibits better performance in most cases and that the gray wolf algorithm can make the results of the support vector machine more accurate.

As an indispensable part of the power system, transformers need to be continuously monitored to detect anomalies or faults in a timely manner to avoid serious damage to the power grid and society. This article proposes a combined model for transformer state prediction, which integrates gas concentration prediction and fault diagnosis models. First, based on the historical monitoring data, each characteristic gas sequence is subjected to one optimal variational mode decomposition (OVMD) and one complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The decomposed sub-sequences are input into a bi-directional long short-term memory network (Bi-LSTM) optimized by the sparrow search algorithm (SSA) for prediction, and the predicted value of each sub-sequence was then superimposed to be the predicted value of the characteristic gas. We input the predicted values of each gas into the improved sparrow search algorithm-optimized support vector machine (ISSA-SVM) model, which can output the final fault type. After the construction of the combined model of state prediction is completed, this paper uses three actual cases to test the model, and at the same time, it uses the traditional fault diagnosis methods to judge the cases and compare these methods with the model in this paper. The results show that the combined model of transformer state prediction constructed in this paper is able to predict the type of transformer faults in the future effectively, and it is of great significance for the practical application of transformer fault type diagnosis.

Purpose: The aim of this study was to evaluate the willingness of parents to allow their children to receive a COVID-19 vaccine and the factors affecting the decision. Materials and Methods: This study was conducted as a prospective, cross-sectional and descriptive survey study. A survey consisting of questions about the COVID-19 vaccination was conducted with parents. Results: The study included 592 parents, 180 (30.4%) male, and the mean age was 34.14±7.42 years. There were 257 (43.4%) parents would vaccinate their children. A high level of education of parents (p=0.022), a high monthly income of the family (p=0.006), and male gender (p=0.002) were associated with high willingness to vaccinate their children. Two hundred twenty two parents stated that their decision may change depending on whether the vaccine is of domestic or foreign origin, and 215 (96.85%) of them stated that they would prefer domestic vaccines. The most frequent reasons for vaccine hesitancy or refusal to their children were: fear of possible side effects and it being a foreign vaccine. Conclusion: The hesitancy or refusal of parents to vaccinate their children with the COVID-19 vaccine is high. Public health authorities should inform the public about the importance of vaccination and domestic vaccine production should be encouraged.