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Wildfire prediction plays a vital role in the management and conservation of forest ecosystems. By providing detailed risk assessments, it contributes to the reduction of fire frequency and severity, safeguards forest resources, supports ecological stability, and ensures human safety. This study systematically reviews wildfire prediction literature from 2003 to 2023, emphasizing research trends and collaborative trends. Our findings reveal a significant increase in research activity between 2019 and 2023, primarily driven by the United States Forest Service and the Chinese Academy of Sciences. The majority of this research was published in prominent journals such as the International Journal of Wildland Fire, Forest Ecology and Management, Remote Sensing, and Forests. These publications predominantly originate from Europe, the United States, and China. Since 2020, there has been substantial growth in the application of machine learning techniques in predicting forest fires, particularly in estimating fire occurrence probabilities, simulating fire spread, and projecting post-fire environmental impacts. Advanced algorithms, including deep learning and ensemble learning, have shown superior accuracy, suggesting promising directions for future research. Additionally, the integration of machine learning with cellular automata has markedly improved the simulation of fire behavior, enhancing both efficiency and precision. The profound impact of climate change on wildfire prediction also necessitates the inclusion of extensive climate data in predictive models. Beyond conventional studies focusing on fire behavior and occurrence probabilities, forecasting the environmental and ecological consequences of fires has become integral to forest fire management and vital for formulating more effective wildfire strategies. The study concludes that significant regional disparities in knowledge exist, underscoring the need for improved research capabilities in underrepresented areas. Moreover, there is an urgent requirement to enhance the application of artificial intelligence algorithms, such as machine learning, deep learning, and ensemble learning, and to intensify efforts in identifying and leveraging various wildfire drivers to refine prediction accuracy. The insights generated from this field will profoundly augment our understanding of wildfire prediction, assisting policymakers and practitioners in managing forest resources more sustainably and averting future wildfire calamities. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The rapid change in artificial intelligence has evaluated ideological and political education ability in colleges and universities as a significant challenge. OBJECTIVES: To assess the level of competence of universities in ideological and political education to determine the effectiveness and efficacy of educational programs and to provide a basis for improving and upgrading academic competence. METHODS: Based on the CIPP model, the author constructed an index system and selected a suitable evaluation model to conduct a study on the evaluation of ideological and political competence of colleges and universities in the context of Artificial Intelligence, which helps to understand the background conditions, resource allocation, teaching activities and quality of teaching of educational programs, as well as the level of ideological and political literacy of the students and their achievements. RESULTS: The evaluation results show that this kind of evaluation research helps to improve and enhance the capacity of ideological and political education in colleges and universities, and at the same time, it can dig into the implementation effect of the educational program, find problems and shortcomings, and promote the continuous improvement of the educational program. CONCLUSION: Through evaluation, the quality and level of ideological and political education in colleges and universities can improve students' ideological and political literacy and sense of social responsibility. In addition, based on this, it makes the development of ideological and political ability in colleges and universities can be better adapted to the era of artificial intelligence. [ABSTRACT FROM AUTHOR]

The Central Business Districts (CBDs) are important hubs for urban economic activities. In the context of globalization, a unified CBD boundary would greatly facilitate the study of global CBD comparative analysis, urban socio-economic development, urban transport and commuting mode. However, previous studies have encountered challenges in developing a practical method for global CBD identification due to limitations in data sources and methodologies. In this study, we selected 32 global megacities as research objects and used the open-access Black Marble nighttime light (NTL) products to construct indicators with the intensity and angular effects of NTL. Clustering and decision tree were then employed to derive rules for CBD identification. Results show that combining Z-score indicators and the strategy of clustering 32 cities before decision tree classification could improve the accuracy of CBD identification, which achieved a producer accuracy of 85%. The 32 cities were clustered into three types, i.e. U.S.A.-like, the mixed type, and China-like. The rules for CBD identification became more complex in the above order, but the accuracy decreased in turn. This study provides a new CBD identification method for cities lacking reference data, allowing for the delineation of unified and comparable CBD boundaries on a large scale. [ABSTRACT FROM AUTHOR]

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Background: Acute radiation dermatitis (ARD) is one of the most common acute adverse reactions in breast cancer patients during and immediately after radiotherapy. As ARD affects patient quality of life, it is important to conduct individualized risk assessments of patients in order to identify those patients most at risk of developing severe ARD. Methods: The data of breast cancer patients who received radiotherapy were prospectively collected and analyzed. Serum ferritin, high-sensitivity C-reactive protein (hs-CRP) levels, and percentages of lymphocyte subsets were measured before radiotherapy. ARD was graded (0–6 grade), according to the Oncology Nursing Society Skin Toxicity Scale. Univariate and multivariate logistic regression analyses were used and the odds ratio (OR) and 95% confidence interval (CI) of each factor were calculated. Results: This study included 455 breast cancer patients. After radiotherapy, 59.6% and 17.8% of patients developed at least 3 (3+) grade and at least 4 (4+) grade ARD, respectively. Multivariate logistic regression analysis found that body mass index (OR: 1.11, 95% CI: 1.01–1.22), diabetes (OR: 2.70, 95% CI: 1.11–6.60), smoking (OR: 3.04, 95% CI: 1.15–8.02), higher ferritin (OR: 3.31, 95% CI: 1.78–6.17), higher hs-CRP (OR: 1.96, 95% CI: 1.02–3.77), and higher CD3 + T cells (OR: 2.99, 95% CI: 1.10–3.58) were independent risk factors for 4 + grade ARD. Based on these findings, a nomogram model of 4 + grade ARD was further established. The nomogram AUC was 0.80 (95% CI: 0.75–0.86), making it more discriminative than any single factor. Conclusion: BMI, diabetes, smoking history, higher ferritin, higher hs-CRP, and higher CD3 + T cells prior to radiotherapy for breast cancer are all independent risk factors for 4 + grade ARD. The results can provide evidence for clinicians to screen out high-risk patients, take precautions and carefully follow up on these patients before and during radiotherapy. [ABSTRACT FROM AUTHOR]

Background: Changes in gastric mucosa caused by Helicobacter pylori (H. pylori) infection affect the observation of early gastric cancer under endoscopy. Although previous researches reported that computer-aided diagnosis (CAD) systems have great potential in the diagnosis of H. pylori infection, their explainability remains a challenge. Objective: We aim to develop an explainable artificial intelligence system for diagnosing H. pylori infection (EADHI) and giving diagnostic basis under endoscopy. Design: A case–control study. Methods: We retrospectively obtained 47,239 images from 1826 patients between 1 June 2020 and 31 July 2021 at Renmin Hospital of Wuhan University for the development of EADHI. EADHI was developed based on feature extraction combining ResNet-50 and long short-term memory networks. Nine endoscopic features were used for H. pylori infection. EADHI's performance was evaluated and compared to that of endoscopists. An external test was conducted in Wenzhou Central Hospital to evaluate its robustness. A gradient-boosting decision tree model was used to examine the contributions of different mucosal features for diagnosing H. pylori infection. Results: The system extracted mucosal features for diagnosing H. pylori infection with an overall accuracy of 78.3% [95% confidence interval (CI): 76.2–80.3]. The accuracy of EADHI for diagnosing H. pylori infection (91.1%, 95% CI: 85.7–94.6) was significantly higher than that of endoscopists (by 15.5%, 95% CI: 9.7–21.3) in internal test. And it showed a good accuracy of 91.9% (95% CI: 85.6–95.7) in external test. Mucosal edema was the most important diagnostic feature for H. pylori positive, while regular arrangement of collecting venules was the most important H. pylori negative feature. Conclusion: The EADHI discerns H. pylori gastritis with high accuracy and good explainability, which may improve the trust and acceptability of endoscopists on CADs. Plain language summary: An explainable AI system for Helicobacter pylori with good diagnostic performance Helicobacter pylori (H. pylori) is the main risk factor for gastric cancer (GC), and changes in gastric mucosa caused by H. pylori infection affect the observation of early GC under endoscopy. Therefore, it is necessary to identify H. pylori infection under endoscopy. Although previous research showed that computer-aided diagnosis (CAD) systems have great potential in H. pylori infection diagnosis, their generalization and explainability are still a challenge. Herein, we constructed an explainable artificial intelligence system for diagnosing H. pylori infection (EADHI) using images by case. In this study, we integrated ResNet-50 and long short-term memory (LSTM) networks into the system. Among them, ResNet50 is used for feature extraction, LSTM is used to classify H. pylori infection status based on these features. Furthermore, we added the information of mucosal features in each case when training the system so that EADHI could identify and output which mucosal features are contained in a case. In our study, EADHI achieved good diagnostic performance with an accuracy of 91.1% [95% confidence interval (CI): 85.7–94.6], which was significantly higher than that of endoscopists (by 15.5%, 95% CI: 9.7–21.3%) in internal test. In addition, it showed a good diagnostic accuracy of 91.9% (95% CI: 85.6–95.7) in external tests. The EADHI discerns H. pylori gastritis with high accuracy and good explainability, which may improve the trust and acceptability of endoscopists on CADs. However, we only used data from a single center to develop EADHI, and it was not effective in identifying past H. pylori infection. Future, multicenter, prospective studies are needed to demonstrate the clinical applicability of CADs. [ABSTRACT FROM AUTHOR]

Background: Changes in gastric mucosa caused by Helicobacter pylori (H. pylori) infection affect the observation of early gastric cancer under endoscopy. Although previous researches reported that computer-aided diagnosis (CAD) systems have great potential in the diagnosis of H. pylori infection, their explainability remains a challenge. Objective: We aim to develop an explainable artificial intelligence system for diagnosing H. pylori infection (EADHI) and giving diagnostic basis under endoscopy. Design: A case–control study. Methods: We retrospectively obtained 47,239 images from 1826 patients between 1 June 2020 and 31 July 2021 at Renmin Hospital of Wuhan University for the development of EADHI. EADHI was developed based on feature extraction combining ResNet-50 and long short-term memory networks. Nine endoscopic features were used for H. pylori infection. EADHI’s performance was evaluated and compared to that of endoscopists. An external test was conducted in Wenzhou Central Hospital to evaluate its robustness. A gradient-boosting decision tree model was used to examine the contributions of different mucosal features for diagnosing H. pylori infection. Results: The system extracted mucosal features for diagnosing H. pylori infection with an overall accuracy of 78.3% [95% confidence interval (CI): 76.2–80.3]. The accuracy of EADHI for diagnosing H. pylori infection (91.1%, 95% CI: 85.7–94.6) was significantly higher than that of endoscopists (by 15.5%, 95% CI: 9.7–21.3) in internal test. And it showed a good accuracy of 91.9% (95% CI: 85.6–95.7) in external test. Mucosal edema was the most important diagnostic feature for H. pylori positive, while regular arrangement of collecting venules was the most important H. pylori negative feature. Conclusion: The EADHI discerns H. pylori gastritis with high accuracy and good explainability, which may improve the trust and acceptability of endoscopists on CADs. An explainable AI system for Helicobacter pylori with good diagnostic performance Helicobacter pylori (H. pylori) is the main risk factor for gastric cancer (GC), and changes in gastric mucosa caused by H. pylori infection affect the observation of early GC under endoscopy. Therefore, it is necessary to identify H. pylori infection under endoscopy. Although previous research showed that computer-aided diagnosis (CAD) systems have great potential in H. pylori infection diagnosis, their generalization and explainability are still a challenge. Herein, we constructed an explainable artificial intelligence system for diagnosing H. pylori infection (EADHI) using images by case. In this study, we integrated ResNet-50 and long short-term memory (LSTM) networks into the system. Among them, ResNet50 is used for feature extraction, LSTM is used to classify H. pylori infection status based on these features. Furthermore, we added the information of mucosal features in each case when training the system so that EADHI could identify and output which mucosal features are contained in a case. In our study, EADHI achieved good diagnostic performance with an accuracy of 91.1% [95% confidence interval (CI): 85.7–94.6], which was significantly higher than that of endoscopists (by 15.5%, 95% CI: 9.7–21.3%) in internal test. In addition, it showed a good diagnostic accuracy of 91.9% (95% CI: 85.6– 95.7) in external tests. The EADHI discerns H. pylori gastritis with high accuracy and good explainability, which may improve the trust and acceptability of endoscopists on CADs. However, we only used data from a single center to develop EADHI, and it was not effective in identifying past H. pylori infection. Future, multicenter, prospective studies are needed to demonstrate the clinical applicability of CADs. [ABSTRACT FROM AUTHOR]

Aims: Traditional tillage represents a serious threat to the stability of soil ecosystems. Understanding the response mechanisms of soil microbial community assembly to different tillage practices is a major topic of soil ecological research. Methods: Here, we investigated the bacterial community structures and assembly in bulk and rhizosphere soils of soybeans grown under traditional tillage (moldboard plow, MP) and two conservation tillage practices, namely, no-tillage (NT) and ridge tillage (RT), using high-throughput sequencing methods. Results: Compared with MP, NT and RT increased the relative abundances of nitrifying bacteria of Nitrosospira sp. and the nitrogen-fixing bacteria of Mesorhizobium sp., Bradyrhizobium sp. and Burkholderia sp., but decreased the abundance of carbon-degrading bacteria, especially Blastococcus sp., Streptomyces sp. and Sphingomonas sp. The altered functional bacteria were mostly affiliated with biomarkers and keystone taxa in the NT and RT networks. For the results of network properties and assembly processes, we found that NT and RT habited a more stable bacterial network structure and a lower homogenizing dispersal value. Soil pH was the primary factor regulating both the bacterial community structures and assembly processes under the three tillage practices. Conclusions: The soil bacterial community structures and assembly processes were profoundly altered by tillage practices. The changes in functional bacteria indicated that conservation tillage might contribute to soil carbon sequestration, while stimulating nitrogen fixation and nitrification. [ABSTRACT FROM AUTHOR]

"Religion in China" by Adam Yuet Chau explores the central role of "relationality" in Chinese religious life, focusing on the relationships between people, spirits, and spaces. The book challenges traditional approaches that categorize Chinese religions into discrete traditions and instead emphasizes lived religious experiences. Chau argues that understanding Chinese religious practices requires a relational approach that considers the interactions and dynamics between individuals and various religious entities. The book covers topics such as religious diversity, interactions with deities and ancestors, festivals and pilgrimages, ritual service providers, communities and networks, and state-religion relations. While the book is aimed at a wide audience, it may require supplementary materials for readers with limited prior knowledge of Chinese religions. [Extracted from the article]

Aphidius gifuensis is the main enemy of Myzus persieae. While its parasitic rate can be influenced by the hyperparasitoid, Pachyneuron aphidis. As important parts of insects to sense odors from various environments, study of sensilla can lay the foundation of the further study about the parasitic mechanisms, reduce the hyperparasitic rate, and make the most effect usage of A. gifuensis. Here, we give a fundamental study about the morphology of the sensilla on the whole body of male and female P. aphidis. We observed seven main types of sensilla on them totally by using scanning electron microscopy. Including Böhm bristle (BB), chaetica sensilla (ChS), basiconic sensilla (BS), trichoid sensilla (TS), and placodea sensilla (PS), coeleoconica sensilla (CoS), basiconic capitate peg sensilla (BCPS). In addition, TS on antennae can be divided into four subtypes, on wings can be divided into two subtypes. Sensilla were most abundant on the antennae. We observed all types of sensilla on antennae. TS4 was uniporous and PS was multiporous. The other sensilla were nonporous. We did not find sexual dimorphism with regards to sensilla on the antennae except for the location of CoS. In male, CoS situated on the fourth subsegment of flagellum, but on the eighth subsegment in female. In other organs, TS has the largest number. We also found BS on compound eyes and ovipositor, BB on thoracic legs. The possible roles of these sensilla played in life activities are discussed. Our study makes a contribution of the parasitic mechanism of hyperparasitoids. [ABSTRACT FROM AUTHOR]

During the past few years, a great deal of effort has been devoted to the anchoring of catalytic oxidation. In this work, three new catalysts CuPp, MnPp and ZnPp by solvothermal methods with 5, 10, 15, 20‐tetrakis(4‐N‐pyrazolyl)‐phenyl porphyrin (H2Pp) and the corresponding metal salts have been synthesized and structurally characterized. The single crystal structures determined by X‐ray diffraction show the bond distances of M‐N in porphyrin cores determined the conformation of porphyrin rings. We explored the catalytic activity of CoPp, CuPp, MnPp and ZnPp for oxidation of alkylbenzenes. The experimental results display these products exhibit high catalytic activities and selectivities for oxidation of ethylbenzene to acetophenone, and can be reused by filtration without appreciable decrease in catalytic activity and selectivity. [ABSTRACT FROM AUTHOR]