Search Results (71,325)

This study investigates the experiences of a professional learning community (PLC) composed of six secondary math teachers enrolled in a graduate math methods course. Through the discussion of educational texts and collaborative inquiry, the teachers identified classroom challenges they aimed to address through individual action research projects. The PLC provided a supportive environment for teachers to share their processes, receive peer feedback, and collectively reflect. This study underscores the value of action research and PLCs in driving educational improvement. By engaging in structured inquiry within a collaborative setting, teachers gained insights into pedagogical issues, developed targeted incremental interventions, and contributed to the broader discourse on math education pedagogy. The collaborative PLC model facilitated reflective practice, challenged assumptions, and empowered teachers as agents of change. Implications for teacher professional development, instructional practices, and future research directions are discussed. Full article

In the variable frequency motor drive system, because the cable impedance does not match the motor impedance, the reflection wave of the voltage wave will be generated. The superposition of reflected voltage waves can lead to overvoltage at the motor ends, which can damage the insulation structure. In this paper, the equivalent circuit models of cable and stator winding are established, respectively. The overvoltage distribution under different power supply frequencies and cable lengths is simulated and analyzed. The influence mechanism of power supply frequency and cable length on the overvoltage distribution of stator winding are studied. The simulation results show that the overvoltage of the first pulse falling edge will be superimposed on the overvoltage of the second pulse rising edge under high-frequency conditions, resulting in a further increase in the overvoltage. The voltage appears in all coils after the middle of the winding. The ground voltage is up to 1.32 times the input voltage, and the inter-turn voltage is up to 9.2 times the average voltage. The increase in cable length will lead to an increase in ground voltage, but the increase in speed will slow down after exceeding the critical length of 300 m. The maximum ground voltage can reach 1.93 times of the input voltage, which is 3.6% different from the calculation result under ideal conditions. The inter-turn voltage changes with the cable length in an N-shaped manner, up to 185 V. The results of this paper are of great significance to further study the insulation design of generator end input. Full article

The aim of this study is to provide tailored alumina particles suitable for reinforcing the metal matrix film. The sol–gel method was chosen to prepare particles of submicron size and to control crystal structure by calcination. In this study, copper-based metal matrix composite (MMC) films are developed on brass substrates with different electrodeposition times and alumina concentrations. Scanning electron microscopy (FE-SEM) with energy-dispersive spectroscopy (EDS), TEM, and X-ray diffraction (XRD) were used to characterize the reinforcing phase. The MMC Cu-Al₂O₃ films were synthesized electrochemically using the co-electrodeposition method. Microstructural and topographical analyses of pure (alumina-free) Cu films and the Cu films with incorporated Al₂O₃ particles were performed using FE-SEM/EDS and AFM, respectively. Hardness and adhesion resistance were investigated using the Vickers microindentation test and evaluated by applying the Chen–Gao (C-G) mathematical model. The sessile drop method was used for measuring contact angles for water. The microhardness and adhesion of the MMC Cu-Al₂O₃ films are improved when Al₂O₃ is added. The concentration of alumina particles in the electrolyte correlates with an increase in absolute film hardness in the way that 1.0 wt.% of alumina in electrolytes results in a 9.96% increase compared to the pure copper film, and the improvement is maximal in the film obtained from electrolytes containing 3.0 wt.% alumina giving the film 2.128 GPa, a 134% hardness value of that of the pure copper film. The surface roughness of the MMC film increased from 2.8 to 6.9 times compared to the Cu film without particles. The decrease in the water contact angle of Cu films with incorporated alumina particles relative to the pure Cu films was from 84.94° to 58.78°. Full article

Linear compressors exhibit high compression efficiency and low noise characteristics, showcasing broad application prospects in various fields such as aerospace, medicine, household appliances, and more. However, due to the complexity of their structures and operation, the issue of vibration isolation in linear compressors has long been a research challenge within the industry. Addressing this challenge, this paper provides an overview of vibration isolation optimization methods for linear compressors. It delves into the discussion of different vibration sources in linear compressors and their respective measurement techniques. By integrating both single degree of freedom (SDOF) and multiple degree of freedom (MDOF) vibration isolation models, this paper describes both active and passive vibration isolation methods tailored to linear compressors. Furthermore, a feasible optimization approach is proposed. Finally, the paper offers insights into the developmental potential and feasibility of vibration energy recovery strategies. Full article

The motion of an object or camera platform makes the acquired image blurred. This degradation is a major reason to obtain a poor-quality image from an imaging sensor. Therefore, developing an efficient deep-learning-based image processing method to remove the blur artifact is desirable. Deep learning has recently demonstrated significant efficacy in image deblurring, primarily through convolutional neural networks (CNNs) and Transformers. However, the limited receptive fields of CNNs restrict their ability to capture long-range structural dependencies. In contrast, Transformers excel at modeling these dependencies, but they are computationally expensive for high-resolution inputs and lack the appropriate inductive bias. To overcome these challenges, we propose an Efficient Hybrid Network (EHNet) that employs CNN encoders for local feature extraction and Transformer decoders with a dual-attention module to capture spatial and channel-wise dependencies. This synergy facilitates the acquisition of rich contextual information for high-quality image deblurring. Additionally, we introduce the Simple Feature-Embedding Module (SFEM) to replace the pointwise and depthwise convolutions to generate simplified embedding features in the self-attention mechanism. This innovation substantially reduces computational complexity and memory usage while maintaining overall performance. Finally, through comprehensive experiments, our compact model yields promising quantitative and qualitative results for image deblurring on various benchmark datasets. Full article

Forest fires pose a significant threat to forest resources and wildlife. To balance accuracy and parameter efficiency in forest fire detection, this study proposes an improved model, Mcan-YOLO, based on YOLOv7. In the Neck section, the asymptotic feature pyramid network (AFPN) was employed to effectively capture multi-scale information, replacing the traditional module. Additionally, the content-aware reassembly of features (CARAFE) replaced the conventional upsampling method, further reducing the number of parameters. The normalization-based attention module (NAM) was integrated after the ELAN-T module to enhance the recognition of various fire smoke features, and the Mish activation function was used to optimize model convergence. A real fire smoke dataset was constructed using the mean structural similarity (MSSIM) algorithm for model training and validation. The experimental results showed that, compared to YOLOv7-tiny, Mcan-YOLO improved precision by 4.6%, recall by 6.5%, and mAP50 by 4.7%, while reducing the number of parameters by 5%. Compared with other mainstream algorithms, Mcan-YOLO achieved better precision with fewer parameters. Full article

Recent studies have shown the potential of transformer-based neural networks in increasing prediction capacity. However, classical transformers present several problems such as computational time complexity and high memory requirements, which make Long Sequence Time-Series Forecasting (LSTF) challenging. The contribution to the prediction of time series of flood events using deep learning techniques is examined, with a particular focus on evaluating the performance of the Informer model (a particular implementation of transformer architecture), which attempts to address the previous issues. The predictive capabilities of the Informer model are explored and compared to statistical methods, stochastic models and traditional deep neural networks. The accuracy, efficiency as well as the limits of the approaches are demonstrated via numerical benchmarks relating to real river streamflow applications. Using daily flow data from the River Test in England as the main case study, we conduct a rigorous evaluation of the Informer efficacy in capturing the complex temporal dependencies inherent in streamflow time series. The analysis is extended to encompass diverse time series datasets from various locations (>100) in the United Kingdom, providing insights into the generalizability of the Informer. The results highlight the superiority of the Informer model over established forecasting methods, especially regarding the LSTF problem. For a forecast horizon of 168 days, the Informer model achieves an NSE of 0.8 and maintains a MAPE below 10%, while the second-best model (LSTM) only achieves −0.63 and 25%, respectively. Furthermore, it is observed that the dependence structure of time series, as expressed by the climacogram, affects the performance of the Informer network. Full article

Autoimmune thyroid diseases (AITDs) are among the most prevalent organ-specific autoimmune disorders, with thyroid hormones playing a pivotal role in the gastrointestinal system’s structure and function. Emerging evidence suggests a link between AITDs and the gut microbiome, which is a diverse community of organisms that are essential for digestion, absorption, intestinal homeostasis, and immune defense. Recent studies using 16S rRNA and metagenomic sequencing of fecal samples from AITD patients have revealed a significant correlation between a gut microbiota imbalance and the severity of AITDs. Progress in animal models of autoimmune diseases has shown that intervention in the gut microbiota can significantly alter the disease severity. The gut microbiota influences T cell subgroup differentiation and modulates the pathological immune response to AITDs through mechanisms involving short-chain fatty acids (SCFAs), lipopolysaccharides (LPSs), and mucosal immunity. Conversely, thyroid hormones also influence gut function and microbiota composition. Thus, there is a bidirectional relationship between the thyroid and the gut ecosystem. This review explores the pathogenic mechanisms of the gut microbiota and its metabolites in AITDs, characterizes the gut microbiota in Graves’ disease (GD) and Hashimoto’s thyroiditis (HT), and examines the interactions between the gut microbiota, thyroid hormones, T cell differentiation, and trace elements. The review aims to enhance understanding of the gut microbiota–thyroid axis and proposes novel approaches to mitigate AITD severity through gut microbiota modulation. Full article

The DNA repair protein PARP-1 emerged as a valuable target in the treatment of tumor entities with deficiencies of BRCA1/2, such as breast cancer. More recently, the application of PARP inhibitors (PARPi) such as olaparib has been expanded to other cancer entities including colorectal cancer (CRC). We previously demonstrated that PARP-1 is overexpressed in human CRC and promotes CRC progression in a mouse model. However, acquired resistance to PARPi and cytotoxicity-mediated adverse effects limit their clinical applicability. Here, we detailed the role of PARP-1 as a therapeutic target in CRC and studied the efficacy of novel PARPi compounds in wildtype (WT) and DNA repair-deficient CRC cell lines together with the chemotherapeutics irinotecan (IT), 5-fluorouracil (5-FU), and oxaliplatin (OXA). Based on the ComPlat molecule archive, we identified novel PARPi candidates by molecular docking experiments in silico, which were then confirmed by in vitro PARP activity measurements. Two promising candidates (X17613 and X17618) also showed potent PARP-1 inhibition in a CRC cell-based assay. In contrast to olaparib, the PARPi candidates caused no PARP-1 trapping and, consistently, were not or only weakly cytotoxic in WT CRC cells and their BRCA2- or ATR-deficient counterparts. Importantly, both PARPi candidates did not affect the viability of nonmalignant human colonic epithelial cells. While both olaparib and veliparib increased the sensitivity of WT CRC cells towards IT, no synergism was observed for X17613 and X17618. Finally, we provided evidence that all PARPi (olaparib > veliparib > X17613 > X17618) synergize with chemotherapeutic drugs (IT > OXA) in a BRCA2-dependent manner in CRC cells, whereas ATR deficiency had only a minor impact. Collectively, our study identified novel lead structures with potent PARP-1 inhibitory activity in CRC cells but low cytotoxicity due to the lack of PARP-1 trapping, which synergized with IT in homologous recombination deficiency. Full article

Corrosive environments can adversely affect the fatigue performance of bridges and other building structures. In order to determine the influence of corrosion on the fatigue failure of concrete composite girders with a corrugated web on a steel bottom plate (hereinafter referred to as CGCWSB), a scaled model test was conducted on a CGCWSB with a span of 30 m, which served as the structural prototype. Through the model test, theoretical analysis, and numerical simulation, the influence of uniform corrosion and pitting corrosion on the fatigue failure of the CGCWSB was determined, and the propagation law of pitting fatigue crack was determined. The results show that (1) the uniform corrosion caused the stress of the CGCWSB to become larger and the performance of the CGCWSB was reduced, the stress growth of the test girder after corrosion was about 10%, the corrosion rate was 9%, the pitting unevenness coefficient was 1.25, and the relative corrosion life was 26.34 years; (2) the fatigue failure of the non-corroded girder belongs to the weld fatigue failure, and the fatigue failure of the corroded girder was the coexistence of weld fatigue failure and pitting fatigue failure; (3) uniform corrosion did not create a new fatigue source, but it did result in the test girder’s fatigue failure ahead of time. Pitting corrosion did, however, create a new fatigue source; (4) an exponential correlation was present between the propagation length of a pitting crack and the number of equal load cycles. The ultimate failure mode of a pitting fatigue crack was when the crack length reached the thickness of the plate and the component was torn and destroyed; (5) following corrosion, the fatigue life of the test girder was found to be reduced by 10.65%, which suggests that salt corrosion had a significant impact on the fatigue life of the composite girder. This research work can provide a reference for the design and promotion of the use of the CGCWSB. Full article

Background: Posttraumatic stress disorder (PTSD) and/or specific PTSD symptoms may evoke maladaptive behaviors (e.g., compulsive buying, disordered eating, and an unhealthy lifestyle), resulting in adverse cardiometabolic events (e.g., hypertension and obesity), which may implicate the treatment of this complex condition. The diagnostic criteria for PTSD have lately expanded beyond the three common symptoms (intrusion, avoidance, and hyperarousal). Including additional symptoms such as emotional numbing, sleep disturbance, and irritability strengthens the representation of the Impact of Event Scale-Revised (IES-R), suggesting that models with four, five, or six dimensions better capture its structure compared to the original three-dimensional model. Methods: Using a convenience sample of 58 Russian dental healthcare workers (HCWs: mean age = 44.1 ± 12.2 years, 82.8% females), this instrumental study examined the convergent, concurrent, and criterion validity of two IES-R structures: IES-R3 and IES-R6. Results: Exploratory factor analysis uncovered five factors, which explained 76.0% of the variance in the IES-R. Subscales of the IES-R3 and the IES-R6 expressed good internal consistency (coefficient alpha range = 0.69–0.88), high convergent validity (item total correlations r range = 0.39–0.81, and correlations with the IES-R’s total score r range = 0.62–0.92), excellent concurrent validity through strong correlations with the PTSD Symptom Scale-Self Report (PSS-SR: r range = 0.42–0.69), while their criterion validity was indicated by moderate-to-low correlations with high body mass index (BMI: r range = 0.12–0.39) and the diagnosis of hypertension (r range = 0.12–0.30). In the receiver-operating characteristic (ROC) curve analysis, all IES-R models were perfectly associated with the PSS-SR (all areas under the curve (AUCs) > 0.9, p values < 0.001). The IES-R, both hyperarousal subscales, and the IES-R3 intrusion subscale were significantly associated with high BMI. Both avoidance subscales and the IES-R3 intrusion subscale, not the IES-R, were significantly associated with hypertension. In the two-step cluster analysis, five sets of all trauma variables (IES-R3/IES-R6, PSS-SR) classified the participants into two clusters according to their BMI (normal weight/low BMI vs. overweight/obese). Meanwhile, only the IES-R, PSS-SR, and IES-R3 dimensions successfully classified participants as having either normal blood pressure or hypertension. Participants in the overweight/obese and hypertensive clusters displayed considerably higher levels of most trauma symptoms. Input variables with the highest predictor importance in the cluster analysis were those variables expressing significant associations in correlations and ROC analyses. However, neither IES-R3 nor IES-R6 contributed to BMI or hypertension either directly or indirectly in the path analysis. Meanwhile, age significantly predicted both health conditions and current smoking. Irritability and numbing were the only IES-R dimensions that significantly contributed to current smoking. Conclusions: The findings emphasize the need for assessing the way through which various PTSD symptoms may implicate cardiometabolic dysfunctions and their risk factors (e.g., smoking and the intake of unhealthy foods) as well as the application of targeted dietary and exercise interventions to lower physical morbidity in PTSD patients. However, the internal and external validity of our tests may be questionable due to the low power of our sample size. Replicating the study in larger samples, which comprise different physical and mental conditions from heterogenous cultural contexts, is pivotal to validate the results (e.g., in specific groups, such as those with confirmed traumatic exposure and comorbid mood dysfunction). Full article

Thermal infrared cameras can image stably in complex scenes such as night, rain, snow, and dense fog. Still, humans are more sensitive to visual colors, so there is an urgent need to convert infrared images into color images in areas such as assisted driving. This paper studies a colorization method for infrared images based on a generative adversarial model. The proposed dual-branch feature extraction network ensures the stability of the content and structure of the generated visible light image; the proposed discrimination strategy combining spatial and frequency domain hybrid constraints effectively improves the problem of undersaturated coloring and the loss of texture details in the edge area of the generated visible light image. The comparative experiment of the public infrared visible light paired data set shows that the algorithm proposed in this paper has achieved the best performance in maintaining the consistency of the content structure of the generated image, restoring the image color distribution, and restoring the image texture details. Full article

In multi-source fusion deformation-monitoring methods that utilize fiber Bragg grating (FBG) data and other data types, the lack of FBG constraint points in edge regions often results in inaccuracies in fusion results, thereby impacting the overall deformation-monitoring accuracy. This study proposes a multi-source fusion deformation-monitoring calibration method and develops a calibration model that integrates vision and FBG multi-source fusion data. The core of this model is a normal distribution transform (NDT)–convolutional neural network (CNN)–self-attention (SA) calibration network. This network enhances continuity between points in point clouds using the NDT module, thereby reducing outliers at the edges of the fusion results. Experimental validation shows that this method reduces the absolute error to below 0.2 mm between multi-source fusion calibration results and high-precision measured point clouds, with a confidence interval of 99%. The NDT-CNN-SA network offers significant advantages, with a performance improvement of 36.57% over the CNN network, 14.39% over the CNN–gated recurrent unit (GRU)–convolutional block attention module (CBAM) network, and 9.54% over the CNN–long short term memory (LSTM)–SA network, thereby demonstrating its superior generalization, accuracy, and robustness. This calibration method provides smoother and accurate structural deformation data, supports real-time deformation monitoring, and reduces the impact of assembly deviation on product quality and performance. Full article

In the industrial sector, malfunctions of equipment that occur during the production and operation process typically necessitate human intervention to restore normal functionality. However, the question that follows is how to design and optimize the intervention measures based on the modeling of actual intervention scenarios, thereby effectively resolving the faults. In order to address the aforementioned issue, we propose an improved heuristic method based on a causal generative model for the design of optimal intervention, aiming to determine the best intervention measure by analyzing the causal effects among variables. We first construct a dual-layer mapping model grounded in the causal relationships among interrelated variables to generate counterfactual data and assess the effectiveness of intervention measures. Subsequently, given the developed fault intervention scenarios, an adaptive large neighborhood search (ALNS) algorithm employing the expected improvement strategy is utilized to optimize the interventions. This method provides guidance for decision-making during equipment operation and maintenance, and the effectiveness of the proposed model and search strategy have been validated through tests on the synthetic datasets and satellite attitude control system dataset. Full article

The pool-type demonstrative China Fast Reactor 600 (CFR-600) adopted a series of improved safety designs, among which the hydraulic suspended passive shutdown subassembly (HS-PSS) is employed for inherent safety enhancement, especially suitable against unprotected loss of flow accident (ULOF). In this article, functional requirements for HS-PSS design in hydro-dynamic aspects are proposed, with the corresponding performance indicators discussed. To address these functional requirements, qualitative analysis on the equilibrium solution properties of the nonlinear dynamical model equation of the hydraulic moving body (HMB) in the HS-PSS are conducted, which leads to the determination of an applicable design parameter domain of the HMB for its practical design from the broad range of structural and parametric design options available for HS-PSS. Furtherly, hydraulically characterizing and modeling the constituent paths and consequent fluid network, hydraulic characteristics of the HS-PSS, as well as the coupled hydro-dynamic motion behaviors of the HMB for suspension and dropping states, were simulated and test-validated. Considering the HS-PSS hydro-dynamic behaviors, key indicators such as critical flowrate, drop time, hydraulic self-tightening performance, as well as the hydraulic characteristics curve are for fulfilling the functional requirements. Meanwhile, through sensitivity study of some structural parameters‘ impact on hydraulic characteristics, some most sensible structural parameters for adjusting and optimizing detailed design are observed. The work is quite significant in supporting the conceptual design of the HS-PSS as well as its engineering improvement. Full article