Analyzing and visualizing dissemination patterns and emerging trends on typo-morphology studies in China

Beyond the long development history from Conzen's morphology to Muratori and Caniggia's typology in Europe, the attention on understanding the continuity of urban form from Chinese scholars are emerging noticeably. It is worth to mention that although there are several articles about the application of typo-morphology into the Chinese context, the work of the literature review is apparently waiting for a more comprehensive and objective study. Thus, a better collecting and demonstrating of the typo-morphology works of literature is urgently requested by tracing the evolution process and dissemination pattern in the Chinese academic community. This study establishes a quantitative study and visual survey by offering abundant visualized graphics about citations and authorship patterns, and relevant bibliography based on the database of Web of Science (WoS) and China National Knowledge Infrastructure (CNKI) by utilizing Citespace. It provides an in-depth analysis of the current theoretical background aiming to inspire further typo-morphological research and practices in the Chinese context and beyond

Karar Ağacı Algoritması Kullanılarak Çin Topraklarındaki Orta Dereceli Okul Öğrencilerine İlişkin Jeo-Uzamsal Düşünme Yeteneğinin Tahmin Edilmesi

Predicting secondary school students' geospatial thinking ability can provide targeted guidance for teachers. To date, few scholars have focused on predicting students’ geospatial thinking ability. In this paper, we address this gap by constructing a prediction model based on the decision tree algorithm, to predict the geospatial thinking ability of secondary school students. A total of 1029 secondary school students were surveyed using the Spatial Thinking Ability Test, the Students' Geography Learning Status Questionnaire, and the Middle Students Motivation Test. Our model indicates that geospatial thinking ability can be predicted by nine factors, in order of importance: academic achievement in geography, geography learning strategy, geography classroom environment, gender, learning initiative, learning goals, extra-curricular time spent learning geography, ego-enhancement drive, and interest in learning geography. The model accuracy is 81.25%. Specifically, our study is the first to predict geospatial thinking ability. It provides a tool for teachers that can help them identify and predict students' geospatial thinking ability, which is conducive to designing better teaching plans and making adjustments to the curriculum.Orta dereceli okul öğrencilerinin jeo-uzamsal düşünme yeteneklerinin tahmin edilmesi öğretmenler için hedefe yönelik rehberlik sağlayabilir. Şimdiye kadar az sayıda bilim insanı, öğrencilerin jeo-uzamsal düşünme yeteneklerinin tahmin edilmesine odaklanmıştır. Bu makalede, orta dereceli okul öğrencilerinin jeo-uzamsal düşünme yeteneklerinin tahmin edilmesi amacıyla karar ağacı algoritmasına dayanan bir tahmin modeli oluşturarak bu boşluğu doldurmayı amaçlıyoruz. Uzamsal Düşünme Yeteneği Testi, Öğrencilerin Coğrafya Öğrenimi Durumu Anketi ve Orta Dereceli Okul Öğrencileri Motivasyon Testi kullanılarak toplam 1029 orta dereceli okul öğrencisine anket uygulanmıştır. Modelimiz, jeo-uzamsal düşünme yeteneğinin dokuz etmenle tahmin edilebileceğine işaret etmektedir. Önem sırasına göre bu etmenler; coğrafya dersindeki akademik başarı, coğrafya öğrenimi stratejisi, coğrafya sınıf ortamı, cinsiyet, öğrenme inisiyatifi, öğrenme hedefleri, coğrafya öğreniminde harcanan müfredat harici zaman, benlik geliştirme dürtüsü ve coğrafya öğrenimine ilgi şeklindedir. Model doğruluk oranı %81,25’tir. Özellikle, çalışmamız jeo-uzamsal düşünme yeteneğinin tahmin edilmesine yönelik ilk çalışmadır. Öğretmenlere öğrencilerin jeo-uzamsal düşünme yeteneklerini saptamalarına ve tahmin etmelerine yardımcı olabilecek bir araç sunan çalışmamız böylelikle daha iyi eğitim planları hazırlanmasında ve müfredatta düzenlemeler yapılmasında fayda sağlayacaktır

Analysis of strength and ductility of partially recrystallized Cu-4.5 wt.% Al

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Decoding the dynamics of poleward shifting climate zones using aqua-planet model simulations

Growing evidence indicates that the atmospheric and oceanic circulation experiences a systematic poleward shift in a warming climate. However, the complexity of the climate system, including the coupling between the ocean and the atmosphere, natural climate variability and land-sea distribution, tends to obfuscate the causal mechanism underlying the circulation shift. Here, using an idealised coupled aqua-planet model, we explore the mechanism of the shifting circulation, by isolating the contributing factors from the direct CO2 forcing, the indirect ocean surface warming, and the wind-stress feedback from the ocean dynamics. We find that, in contrast to the direct CO2 forcing, ocean surface warming, in particular an enhanced subtropical ocean warming, plays an important role in driving the circulation shift. This enhanced subtropical ocean warming emerges from the background Ekman convergence of surface anomalous heat in the absence of the ocean dynamical change. It expands the tropical warm water zone, causes a poleward shift of the mid-latitude temperature gradient, hence forces a corresponding shift in the atmospheric circulation and the associated wind pattern. The shift in wind, in turn drives a shift in the ocean circulation. Our simulations, despite being idealised, capture the main features of the observed climate changes, for example, the enhanced subtropical ocean warming, poleward shift of the patterns of near-surface wind, sea level pressure, storm tracks, precipitation and large-scale ocean circulation, implying that increase in greenhouse gas concentrations not only raises the temperature, but can also systematically shift the climate zones poleward

Understanding the dynamic of poleward shifting of atmospheric and oceanic circulation using aqua-planet model simulations

Growing evidence suggests that the oceanic and atmospheric circulation experiences a systematic poleward shift under climate change. However, due to the complexity of climate system, such as, the coupling between the ocean and the atmosphere, natural climate variability and land-sea distribution, the dynamical mechanism of such shift is still not fully understood. Here, using an idealized partially coupled ocean and atmosphere aqua-planet model, we explore the mechanism of the shifting oceanic and atmospheric circulation. We find that, in contrast to the rising GHG concentration, the subtropical ocean warming plays a dominant role in driving the shift in the circulation system. More specifically, due to background ocean dynamics, a relatively faster warming over the subtropical ocean drives a poleward shift in the atmospheric circulation. The shift in the atmospheric circulation in turn drives a shift in the oceanic circulation. Our simulations, despite being idealized, capture the main features of observed climate changes, for example, the enhanced subtropical ocean warming, poleward shift of the patterns of near-surface wind, sea level pressure, cloud, precipitation, storm tracks and large-scale ocean circulation, implying that global warming not only raises the temperature, but also systematically shifts the climate zones

A Deep Learning-Based Fault Diagnosis of Leader-Following Systems

This paper develops a multisensor data fusion-based deep learning algorithm to locate and classify faults in a leader-following multiagent system. First, sequences of one-dimensional data collected from multiple sensors of followers are fused into a two-dimensional image. Then, the image is employed to train a convolution neural network with a batch normalisation layer. The trained network can locate and classify three typical fault types: the actuator limitation fault, the sensor failure and the communication failure. Moreover, faults can exist in both leaders and followers, and the faults in leaders can be identified through data from followers, indicating that the developed deep learning fault diagnosis is distributed. The effectiveness of the deep learning-based fault diagnosis algorithm is demonstrated via Quanser Servo 2 rotating inverted pendulums with a leader-follower protocol. From the experimental results, the fault classification accuracy can reach 98.9%

Weigh-in-Motion Sensor and Controller Operation and Performance Comparison

This research project utilized statistical inference and comparison techniques to compare the performance of different Weigh-in-Motion (WIM) sensors. First, we analyzed test-vehicle data to perform an accuracy check of the results reported by the sensor-vendor Intercomp. The results reported by Intercomp mostly matched with our own analysis, but the data were found to be insufficient to reach any conclusions about the accuracy of the sensor under different temperature and speed conditions. Second, based on the limited data from the Intercomp and IRD sensor systems, we performed tests of self-consistency and comparisons of measurements to inform the selection of a superior system. Intercomp sensor data were found to be not self-consistent but IRD data were. Given the different measurements provided by the two sensors, without additional data, we were not able to reach a conclusion regarding the relative accuracy or the duration of consistent observations before needing recalibration. Initial comparisons indicated potential problems with the Intercomp sensor. We then suggested alternate approaches that MNDOT could use to determine whether recalibration was required. Finally, we analyzed ten-month data from the IRD WIM system and four-month data from the Kistler WIM system to evaluate relative sensor accuracy. While both systems were found to be self-consistent within the data time frame, the Kistler system generated more errors than the IRD system. Conclusions regarding relative accuracy could not be reached without additional data. We identified the sorts of measurements that would need to be monitored for recalibration and the methodology needed for estimating future recalibration time

Almonertinib plus chemotherapy versus almonertinib alone in second-line treatment of advanced non-small cell lung cancer with mutated epidermal growth factor receptor: a retrospective study

ObjectiveThis study mainly observes the efficacy and safety of almonertinib plus chemotherapy compared with almonertinib alone in the second-line treatment of advanced non-small cell lung cancer (NSCLC) with mutated epidermal growth factor receptor (EGFR).MethodsIn this study, clinical data of 68 patients with advanced NSCLC who were treated in Jiangsu Provincial People’s Hospital and Nanjing Chest Hospital between April 2020 and December 2022 were collected. Among them, the study group (n=30) received second-line almonertinib combined with platinum-based chemotherapy, while the control group (n=38) received almonertinib alone. The near-term and long-term effects and adverse events of the two groups were compared respectively.ResultsThe median follow-up time until 31 December 2022 was 16.3 months (95% CI: 11.32-21.34). Results of chi-square analysis showed no statistically significant difference in objective response rate (ORR) and disease control rate (DCR) between the study group and the control group (56.73% vs. 55.3%, P&gt;0.05; 100% vs. 86.8%, P&gt;0.05). Log-rank test comparing the two groups revealed that the median progression-free survival (mPFS) of the study group was significantly longer than that of the control group by 3.1 months (12.7 vs. 9.6 months, P=0.01). Multivariate COX proportional risk model showed a statistically significant effect of treatment method and PS score on PFS (HR=0.43, P=0.023; HR=3.82, P=0.001). In terms of safety, most of the adverse events (AEs) were mild, with no grade 4-5 in the two groups, and the overall tolerance of patients was good.ConclusionFor advanced NSCLC patients with EGFR mutations, second-line treatment with almonertinib plus chemotherapy significantly improved PFS compared with almonertinib alone without a significant increase in adverse events, providing efficacy and safety