19684 research outputs found
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TRPV4 stimulates colonic afferents through mucosal release of ATP and glutamate
No full textBackground and Purpose: Abdominal pain is a leading cause of morbidity for people living with gastrointestinal disease. Whereas the transient receptor potential vanilloid 4 (TRPV4) ion channel has been implicated in the pathogenesis of abdominal pain, the relative paucity of TRPV4 expression in colon-projecting sensory neurons suggests that non-neuronal cells may contribute to TRPV4-mediated nociceptor stimulation. Experimental Approach: Changes in murine colonic afferent activity were examined using ex vivo electrophysiology in tissues with the gut mucosa present or removed. ATP and glutamate release were measured by bioluminescence assays from human colon organoid cultures and mouse colon. Dorsal root ganglion sensory neuron activity was evaluated by Ca2+ imaging when cultured alone or co-cultured with colonic mucosa. Key Results: Bath application of TRPV4 agonist GSK1016790A elicited a robust increase in murine colonic afferent activity, which was abolished by removing the gut mucosa. GSK1016790A promoted ATP and glutamate release from human colon organoid cultures and mouse colon. Inhibition of ATP degradation in mouse colon enhanced the afferent response to GSK1016790A. Pretreatment with purinoceptor or glutamate receptor antagonists attenuated and abolished the response to GSK1016790A when given alone or in combination, respectively. Sensory neurons co-cultured with colonic mucosal cells produced a marked increase in intracellular Ca2+ to GSK1016790A compared with neurons cultured alone. Conclusion and Implications: Our data indicate that mucosal release of ATP and glutamate is responsible for the stimulation of colonic afferents following TRPV4 activation. These findings highlight an opportunity to target the gut mucosa for the development of new visceral analgesics.</p
Fabricating diamond bit by selective laser melting and its drilling performance evaluation
Get PDFDiamond abrasive tools play an important role in efficient and precision machining of difficult-to-cut materials. As the traditional tool manufacturing process is difficult to meet the integrated molding preparation of complex structures, the use of selective laser melting (SLM) technology to manufacture diamond tools with functions and structures has become a research hotspot in recent years. In this paper, the feasibility and wear characteristics of diamond bits prepared by SLM using AlSi7Mg as metal matrix are investigated. The optimal parameter combination of diamond/AlSi7Mg mixture was obtained through orthogonal experiments, in which the powder layer thickness was 30 μm, the laser power was 300 W, the scanning speed was 3000 mm/s, and the scanning spacing was 120 μm. This parameter combination realized the improvement of the mechanical properties and the decrease of the porosity. Two types of bits with and without groove-structure (Labeled as Bit-G and Bit-N) were fabricated and their drilling performance were compared by processing BK7 optical glass. The results show that the Bit-G has more excellent machining performance, including lower drilling force, better hole quality and longer service life. According to the simulation results, the self-sharpening cycle of the Bit-G’s wear characteristics can be attributed to the groove structure that allows the coolant to be fully utilized in the bit/workpiece contacting area. This paper demonstrates the feasibility of the SLM process for the preparation of diamond tools and comprehensively analyzes the wear process, which provides reference value for further research on this process for the manufacturing of diamond abrasive tools.</p
Women’s Double Penalty During Telework:A Mixed Method Investigation of the Gender Effect of Interruptions Between Work and Childcare
Get PDFTelework arrangements remain popular since they have been “normalized” in the context of the pandemic. Telework may help reduce the gender gap in access to work despite women’s prominent role in caring responsibilities. However, the work experience and career effects of such arrangements may also be gendered, particularly given the increased number of cross-domain interruptions that tend to accompany telework. We investigated the gendered effects of cross-domain interruptions between childcare and telework through a mixed methods approach, including a daily diary study with 339 teleworking parents and semi-structured interviews with 16 teleworking mothers and 16 teleworking fathers. We find that childcare-to-work interruptions have negative effects on the fulfillment of career motives, on work engagement and emotional exhaustion, for both men and women. The effects of work-to-childcare interruptions are, however, different for men compared to women, with only women’s perceived daily balance being negatively affected. Interestingly, men even benefit from some positive effects of these interruptions, which allow them to experience more daily authenticity and challenge. Our qualitative findings help to interpret these findings by suggesting gender motive differences with women reporting more relational and (to a smaller extent) uncertainty related work motives. The interview data also illustrate how various approaches to the division of household labor and boundary management may contribute to gendered interruption experiences. Overall, these findings illustrate how the daily experiences of teleworkers can contribute to growing gender gaps in terms of career and wellbeing.</p
Predicting Academic Performance of University Students Using Adaptive Neuro Fuzzy Inference System (ANFIS)- Subtractive Clustering Algorithm (ANFIS-SC):A Case Study in the UK
No full textIn academic institutions, among the most important success criteria is students’ academic performance. However, one of the biggest challenges facing institutions has been the early detection and enhancement of students' academic performance at all levels. Students may run into several issues that hinder their ability to study, hereby, having a detrimental effect on their academic achievement. These problems can be effectively resolved if student data is pre-analyzed with early performance predictions, to enable prompt support decisions. Thus, this work applied an Adaptive Neuro Fuzzy Inference System (ANFIS) with subtractive clustering to predict students’ academic performance and identify factors that influences the students’ performance. Hence, this would be helpful in making inform decisions that support students who require assistance; also, taking effective steps to improving their academic performance. Furthermore, due to the benefits of mixing both neural networks and fuzzy systems, the applied ANFISmodel, which is a hybrid learning algorithm processes information quickly to produce more comprehensible and interpretable insight. Also, subtractive clustering (SC) was used to group similar characteristics in the dataset, to decrease the number of rules and membership functions of ANFIS; hereby, reducing the complexity of ANFIS. Academic records of computer-science students at the University of Huddersfield from 2017-2022 were used, which provided several features useful in predicting the students’ performance. Evaluation the results of ANFIS-SC with recommended machine learning techniques in articles showed that Decision Tree, Ada Boost Regression, Neural Network have a high accuracy score, likewise, the Adaptive Neuro Fuzzy Inference System with Subtractive Clustering (ANFIS-SC)
Contextualizing urban road network hierarchy and its role for sustainable transport futures:A systematic literature review using bibliometric analysis and content analysis tools
No full textUrban road networks play a crucial role in transport and urban planning and have the potential to contribute to more sustainable futures if their hierarchy is properly understood. However, the concept of the urban road network hierarchy, which refers to street classification and prioritization, is not well defined within the domain of transport engineering management, leaving many questions unanswered. Is it simply a planning tool, or does it extend to defining the essence of cities? Is it a qualitative or quantitative concept? Does it emerge organically or require proactive planning? Given the lack of comprehensive answers to these questions, this research aims to provide a contextual understanding of the urban road network hierarchy through the lens of sustainable transport futures. To this purpose, we conducted a systematic literature review, which is an effective method for consolidating knowledge on a specific topic. A total of 42 articles were analyzed using both quantitative bibliometric analysis and qualitative content analysis. Our work demonstrates that the road network hierarchy consists of 16 sub-concepts. Four main research trends were identified and discussed: a) road morphology and structure, b) advanced algorithms for street classification, c) integrated street classification planning, and d) the social dimension of street classification. Recent literature indicates a shift toward alternative road network hierarchy approaches that prioritize sustainable mobility over car-centric models. In conclusion, our analysis reveals that the urban road network hierarchy is a multifaceted yet under researched “vehicle for change,” which, if utilized effectively, offers opportunities to reimagine urban road environments.</p
Rail surface damage management through monitoring and modelling
Get PDFRolling contact fatigue (RCF) cracking and wear significantly impact rail surfaces. The interaction between them is crucial; wear dominance can remove initiated cracks, while low wear rates may escalate crack propagation, elevating rail failure risks. Grinding, a preventive maintenance technique, removes remaining cracks, applied in fixed-interval or condition-based regimes. Operational constraints, favouring cyclic regimes, may potentially reduce rail life and increase costs. Thus, an optimal asset management strategy involves grinding based on current RCF condition and its predicted growth rate, considering its interaction with wear. London Underground employs diverse rail inspection methods, including advanced MRX-RSCM technology, to measure crack depth. Research used its measurements, developing a novel Combined Shakedown Map and Tγ approach. This predicts RCF growth under wear interaction, indicating the material to be removed by grinding. Its results are displayed for high and low rails of curved track sites, compared with monitoring measurements, enabling observation of changes in damage susceptibility and related grinding requirements over different rails
Study of cutting force predictability, signal complexity of different end milling CWE stages with different modelling methods
No full textCutting force analysis in milling processes is essential for precision metal cutting as it contributes to understanding tool wear, optimising machining performance, and ensuring overall process stability. Numerous research papers have been published to describe modelling techniques that provide high-fidelity predictions, with recent developments highlighting the benefits of combining different methods. However, these approaches are relatively limited in their ability to predict over the wide frequency range needed to describe the tooth passing frequency (TPF) and its harmonics under varying working conditions or stages of cutter-workpiece engagement (CWE). This paper studies the prediction performance of different modelling techniques when considering wide-band noise under varying working conditions. The methods evaluated are the explicitly defined but difficult-to-parameterise Finite Element Method (FEM), Semi-Analytical Solutions (SAS), and Long Short-Term Memory (LSTM) networks, which are black-box deep learning methods incorporating time-based information. Since white-box models are still more readily adopted by industry, the paper also introduces a new post-processing model to improve the prediction accuracy of FEM and SAS based upon the Fourier Series of the TPF (FS-TPF). Over the observable range of 0 to 1500 Hz, the cutting force predictability was assessed in both the time and frequency domains using similarity of frequency distribution, Shannon entropy, and Kullback–Leibler (KL) divergence. Verification and analysis indicate that the cutting force predictability with FEM at “partial engagement” was the lowest, due to its lack of ability to describe TPF harmonics. In contrast, the LSTM model showed the best prediction performance across all tested working conditions. The new FS-TPF significantly increased FEM’s prediction accuracy by approximately 50% and improved SAS’s performance by 20%. Finally, a Deep Neural Network (DNN) is compared to the LSTM, suggesting that both methods are suitable for force prediction without encountering significant accuracy issues across the different stages of CWE. It was found that the key to increasing cutting force predictability to be generally applicable to all milling conditions is the capability to describe TPF harmonics across the different CWE stages in milling processes. The FS-TPF compensation can dramatically enhance the cutting force prediction accuracy of FEM and SAS, while the applied DL-LSTM and DNN models have successfully demonstrated their wide adaptability without requiring additional post-processing.</p
Adventures in Xenophobia:The Light at the Edge of the World (1971) as a Cultural Artifact
No full textHybrid Solver Techniques for Wideband Antennas
No full textEvaluating the performance of an antenna using commercially available electromagnetic (EM) solvers such as Computer simulation technology (CST) studio suite has become crucial. In this context, a unidirectional advanced technique is used for simulating a Printed Log-Periodic Antenna (PLPDA) enclosed in a fixture with foam. This method employs a hybrid solver approach, in which the PLPDA with foam (source) is simulated using a transient solver, and the fixture (platform) is simulated using an integral equation solver. The proposed technique significantly reduces the simulation time and RAM usage while maintaining high accuracy. It is a viable alternative to traditional full-wave analysis, where the source and platform are simulated using a single solver or bidirectional simulation techniques. A comparative analysis with measured radiation patterns demonstrates the effectiveness of this method, providing a reliable and efficient solution for antenna design and performance evaluation
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