Issue Features Research Articles

Information Photonics: introduction to the feature issue

This feature issue of Applied Optics is dedicated to the international meeting of Information Photonics 2023 (IP2023) that was held on the 8th and 9th of September 2023 in New Taipei, Taiwan. IP2023 covers various topics, including information acquisition, information processing, metaverse photonics, information display, optical imaging, and optical metrology.

Improved Cascade-RCNN for automatic detection of coronary artery plaque in multi-angle fusion CPR images

Coronary heart disease is a disease that seriously endangers human health and life which is caused by plaque formation due to coronary artery atherosclerosis or spasm. The detection of coronary plaques through medical imaging is a non-destructive and fast diagnostic method, which holds significant medical and clinical value for the diagnosis of coronary heart disease. In this paper, We proposed an automatic detecting method for coronary artery vulnerable plaque based on CTA image sequence. We collected CTA image sequences from 132 patients and obtained the coronary artery tree by performing three-dimensional (3D) segmentation. The coronary artery centerline was extracted using the skeleton thinning method. To address the issue of insufficient features in a single CPR image, we constructed Curved Planar Reconstructed (CPR) images from multiple directions perpendicular to the coronary artery, following the centerline. In total, we established 8797 images in the training dataset and 1553 images in the test dataset. We proposed an attentive detection method that fused a new Cascade RCNN ResNet50-FPN neural network model with a convolutional attention module (CBAM). This method achieved automatic recognition and detection of plaques on the two-dimensional multi-angle fused CPR images. The proposed ResNet50-FPN network performed feature extraction, and CBAM was added after the first and last convolutional layers to enhance the network’s attention to plaque features. Our experimental results demonstrate that deep learning neural networks can provide a feasible approach to automatically detecting coronary artery plaques. Our method achieved a plaque detection accuracy of 94.6%, outperforming the RCNN method proposed by Zriek.

Immediate shock and residual stress: Unmasking the dual dynamics of covid-19 on the mental health of health professionals in China.

The pandemic of 2019 has created large scale disruption around the globe. It has posed extraordinary hurdles for Chinese health workers, affecting their mental health with respect to situations in the present as well as the future. Recognising the dynamic features of these mental health issues among health professionals is critical for developing effective support systems. This study aims to explore the dual impact of COVID-19 on the mental health of health professionals in China, both during and after the pandemic; to explore the probable underlying factors causing these mental health issues; to identify both adaptive and maladaptive coping behaviours employed by the health personnel; and to assess the organisational and social support to mitigate the effect of the pandemic on the mental health of the Chinese health professionals. To address these objectives an explorative qualitative research design has been adopted involving analysis of previous researches in the relevant field. The study confirms anxiety, depression, sleep disorders, PTSD, and burnout among medical personnel as immediate shock and residual stress, along with its demographic variations. Few maladaptive coping behaviours have been identified that can lead to delayed psychological effects even after a pandemic. The key findings underscore the complicated interaction of immediate and delayed mental health challenges among health professionals, advocating for the deployment of tailored treatments and policy modifications to address the ever changing needs of this vital workforce.

Editorial: Inaugural Issue of the International Journal of Bridge Engineering, Management and Research (IJBEMR)

It is my pleasure to share with you the inaugural issue of the newly established International Journal of Bridge Engineering, Management and Research. The International Journal of Bridge Engineering, Management and Research (BER) is a fully open access online-only journal. It serves as a forum for the publication of scientific and technical papers related to all aspects of bridge engineering and management, including structural, seismic, hydraulic and geotechnical risk analysis, structural health monitoring, static and dynamic assessments, structural retrofitting, and resiliency enhancement. The journal also publishes research papers on tunnels focusing on the areas mentioned above. Technical research papers and case studies dealing with innovative technological or computational solutions to bridges and tunnels, as well as experimental methods and novel design and theoretical analysis procedures are strong candidates for publication in the journal. This inaugural issue features five high quality papers in bridge engineering. Through this issue, I am also inviting you to register with the journal and submit your research manuscripts to the journal. We are committed to expedited and thorough peer review and publication of your research.

Translation of Children’s Rhyme Bible Storybook on “The Creation” from English into Indonesian

Foreign children’s literature has been translated in various target languages and the most translated stories are from the Bible. The uniqueness of this study is on the rhyming text translation. It is very challenging in translating rhyming text due to distinct features of orality, sociocultural context, lexical and grammatical issues. This paper aims at 1) exploring rhyme types found both in the source text and in the target text; 2) investigating the use of the translation techniques; 3) examining the translation shift; and 4) assessing the translation quality of the rhyming translated text. This is a descriptive qualitative study that applied the content analysis design. The collection of data is through focus group discussion, note-taking and quality translation (QT) rating instrument. Then data are analyzed using analyses of domain, taxonomy, and componential. The findings present 9 data of rhyme types identified in the source text and 10 data of rhyme types in the target text. Besides, there are 12 translation techniques frequently used. Furthermore, the translation shift exists in the translated text indicated by 34 data. Finally, the assessment of translation quality reveals the rates of three aspects of the parameter namely accuracy (2.22), acceptability (3), and readability (3) in which its overall score is 2.61. The overall score of the translation quality reveals that the translation meaningfully succeeds.

Features and Major Issues of Indian Economy

Theodore Roosevelt remarked, “A peep in the past prepares you better for the future”. Thus, for understanding the present state of the Indian economy and its future prospects a brief comment on its past will be appropriate. The Indian economy, before the British came around the middle of the nineteenth century, was quite sound. According to an estimate, the Gross Domestic Product (GDP) of India in the sixteenth century was about 25% of the world economy, the second largest in the world. However, after the arrival of the colonial rule the Indian economy faced disaster; from being an exporter of processed goods it changed to being an exporter of raw materials and a buyer of manufactured goods. As per British economist, Angus Maddison, India’s share of the world income went from 27% in 1700 A.D. (compared to Europe share of 23%) to 3% in 1950. After India got independence in 1947, the process of rebuilding the economy started. However, it was mostly centralized and the period from 1947 to 1991 was termed as the licence raj, because the successive governments followed protectionist economic policies. This trend led to a balance of payments crisis in the year 1990. The year 1991 turned out to be watershed in the Indian economy when the government of India in June 1991 announced major liberalization policies in Indian economy. The emergence of India as a major economic power in the world happens to be one such idea. Since then, Indian economy has progressed immensely with GDP progressing at the rate of 6 to 8% per annum and is expected to be one of the top three economic powers in the world over the next 10-15 years. India is also backed by its robust democracy and strong partnerships. India has finally arrived in the category of fast emerging economies. It is fast entering the league of high growth nations. The future seems to have much more in store. But a lot depends on how the grey areas are tackled. We have finally been able to unshackle ourselves from the stigma of being a low growth economy and have infused dynamism into our economic system. Not only is India achieving higher growth rates year after year, the rate at which it is moving towards higher growth targets is also higher. In this research paper discuss the features of Indian economy and major issues of Indian economy.

FC-StackGNB: A novel machine learning modeling framework for forest fire risk prediction combining feature crosses and model fusion algorithm

Forest fire risk prediction is a crucial link in maintaining forest ecological security. Machine learning, due to its powerful non-linear modeling capabilities, has been widely applied in forest fire risk prediction research. However, existing studies often focus on the direct information provided by multiple environmental factor features when constructing the feature space, while overlooking the deeper information conveyed by feature cross-correlations. Additionally, fire risk prediction predominantly relies on single-model forecasting, exhibiting slightly insufficient generalization and stability in models. Model fusion algorithms (MFA) can combine the advantages of multiple models to compensate for this limitation. In this study, a machine learning framework, FC-StackGNB, combining feature crosses (FC) and model fusion, is proposed. This framework employs the FC method to analyze the temporal trends of various environmental factors influencing fire occurrence, constructing multiple seasonal cross features (SCFs) capable of effectively capturing the non-linear relationship between environmental factors and time. Moreover, the framework develops a Gaussian Naive Bayes (GNB) optimized stacking MFA to fully leverage the strengths of different ML algorithms. Results demonstrate that the introduction of SCFs effectively enhances the prediction performance of six machine learning models, with the mean values of five evaluation metrics (Accuracy, Precision, Recall, F1-score, and ROC_AUC) increasing by 1.58% to 6.30%. The fusion model constructed based on the StackGNB algorithm can effectively handle the multicollinearity issue of features, exhibiting significantly better prediction performance than single models, particularly in improving the Recall metric (increasing by around 3% and 5% compared to the top two ranked single models respectively), which signifies the model’s ability to predict positive samples (i.e., high-risk fire areas). The proposed modeling framework effectively enhances the robustness and prediction performance of the models, offering new modeling insights for subsequent research. This study holds significant importance for enhancing the level of forest fire risk warning.

Advances in Ocular Surface Research Feature Issue.

Advances in Ocular Surface Research Feature Issue.

Feature issue editors.

Feature issue editors.

Introduction to Special Issue: How Research Reform in Psychology Can Influence Professional School Psychology

This special issue features essays by prominent scholars in school psychology discussing how shifts in research can reshape the future training and practice of the field. A profession that evolves and adapts indicates a robust and dynamic discipline. However, staying current with these changes poses a significant challenge. Given the pivotal role of evidence-based practices in school psychology, the type and quality of research directly influence how psychological services are delivered and practiced. Reforming research practices in psychology can serve as a catalyst for enhancing our field. This issue presents a variety of viewpoints on how research and clinical practice intersect in a rapidly changing landscape.

Introduction to the special issue: Asian diaspora theorizing: defying Racism~Re-imagining alternate Nows~Invigorating otherwise futures

ABSTRACT This is an introductory article to Part I of a special issue series entitled, “Asian Diaspora Theorizing: Defying Racism~Re-imagining Alternate Nows~Invigorating Otherwise Futures.” This special issue is a trilogy of our Asian diaspora studies project: Asian diasporas as theories, Asian diasporas as methodologies, and Asian diasporas as praxes. Part II of this special issue will appear in DIME 19(2) in 2025. This special issue features the cutting-edge work of Asian diaspora educational workers who engage in diaspora theorizing, where we rememorize our diaspora histories and critically examine ourselves, our works, and our lives to defy racism, re-imagine alternate nows, and invigorate otherwise futures.

First person profile: Dr Hwu underscores his leadership role as one that is fundamentally about helping to advance clinical and research endeavors by forming and supporting a good team, mentoring, and building alliances.

This news section offers Cancer readers timely information on events, public policy analysis, topical issues, and personalities. This issue features Dr Patrick Hwu, a pioneer in immunotherapy whose studies of gene transfer into immune cells led to CAR‐T and TIL therapies. In addition, a promising new treatment shows potential for high‐risk bladder cancer, and AI is being used to tailor cancer care and drug discovery.

Review of Outpatient Pediatric Ethics Consults at an Academic Medical Center.

Limited data exist in the specific content of pediatric outpatient ethics consults as compared to inpatient ethics consults. Given the fundamental differences in outpatient and inpatient clinical care, we aimed to describe the distinctive nature of ethics consultation in the ambulatory setting. This is a retrospective review at a large, quaternary academic center of all outpatient ethics consults in a 6-year period. Encounter-level demographic data was recorded, and primary ethical issue and contextual features were identified using qualitative conceptual content analysis. A total of 48 consults were identified representing 44 unique patients. The most common primary ethical issue was beneficence and best interest concern comprising 20 (42%) consults, followed by refusal of recommended treatment comprising 11 (23%) consults and patient preference/assent comprising 5 (10%) consults. The most common contextual features were staff-family communication dispute/conflict comprising 28 (58%) consults, followed by legal involvement comprising 25 (52%) consults and quality of life comprising 19 (40%) consults. The most common consulting specialty was hematology/oncology. Ethical issues encountered in the provision of outpatient pediatric care are distinct and differ from those in inpatient consults. Further research is necessary to identify strategies and educational gaps in outpatient ethics consultation to increase its effectiveness and utilization.

Topological Photonics: introduction to the feature issue

We introduce the feature issue of Optical Materials Express on Topological Photonics.

Confiscation of Property: Russian and Foreign Law Enforcement Experience

The paper examines the prospects for increasing the effectiveness of this measure. The study utilizes the data on the use of confiscation of property as another measure of criminal law in Russia, as well as foreign experience in the use of confiscation of property. The author notes the alarming fact that Article 104.1 of the Criminal Code of the Russian Federation is applied in 60% of cases for crimes related to violation of the rules for handling narcotic drugs, psychotropic and potent substances; given the frequency of application of the measure under study, the absence of a significant desired effect is indicated. It is noted that the confiscation of property actually performs only a «protective» function and neglects the potential preventive component. There are several shortcomings pointed out in the paper. Article 104.1 of the Criminal Code of the Russian Federation specifies a closed list of crimes. The legislation restricts the possibility of confiscating property transferred by a convicted person to another person; it is only possible if the person knew that it was obtained as a result of criminal acts; the burden of proof of the illegality of the acquisition of property is placed on government bodies. The obstacles to the return of confiscation of property to the number of punishments are examined separately. These include the experience of using confiscation as another measure of a criminal legal nature that has developed over the past few years. Some more are the possibility of confiscating property for the benefit of the state in a civil-legal manner; the negative historical experience of using confiscation of property; the absence of clearly reliable and recognized data characterizing the preventive potential of confiscation as a punishment (especially in the context of the possibility of using confiscation of property as a punishment exclusively to the guilty person). Some attention is given to the issue of positive and negative features of the regulation of confiscation of property in sectoral legislation, as well as aspects of shifting the burden of proof of the legality of the acquisition of property.

Remembering Jeremy Safran: continuing the conversation.

This introduction provides an overview to this special issue honoring the work and legacy of Jeremy D. Safran. Born of the Jeremy Safran Memorial Conference, held on April 2nd, 2023, this issue features a wide range of contributions from leaders in the field, former students, and early career professionals whose work engages and develops central ideas from Safran's work and reflects on his impact on their own clinical work and scholarship. Themes center around the three domains of Safran's major contributions: pedagogy; psychotherapy integration; and Buddhism, spirituality, and psychoanalysis. We observe among the contributions an experiential reconnecting with the deeply relational commitments of our friend and colleague.

Fault diagnosis method for continuous casting machine’s sector segment based on SG-PCA-LSTM

Abstract To address challenges such as the high variability of variables and difficulties in feature extraction during the casting process of the continuous casting machine’s sector segment, a fault diagnosis method based on SG-PCA-LSTM is proposed. This method aims to overcome the issue of fault features obscured by noise in the total tension time series by employing the SG smoothing algorithm for filtering and denoising. By leveraging the inter-segment data correlation and the advantage of PCA in extracting fault feature information, combined with the powerful learning capability of LSTM in modeling, a Principal Component Analysis - Long Short-Term Memory (PCA-LSTM) fault diagnosis model is established. Through comparative analysis against different diagnostic methods in terms of recognition rate, false positive rate, etc., the results obtained by this method are compared against those obtained by using other algorithms. Experimental results demonstrate that the proposed method exhibits good overall performance in terms of accuracy and training time.

Predicting potential microbe-disease associations based on dual branch graph convolutional network.

Studying the association between microbes and diseases not only aids in the prevention and diagnosis of diseases, but also provides crucial theoretical support for new drug development and personalized treatment. Due to the time-consuming and costly nature of laboratory-based biological tests to confirm the relationship between microbes and diseases, there is an urgent need for innovative computational frameworks to anticipate new associations between microbes and diseases. Here, we propose a novel computational approach based on a dual branch graph convolutional network (GCN) module, abbreviated as DBGCNMDA, for identifying microbe-disease associations. First, DBGCNMDA calculates the similarity matrix of diseases and microbes by integrating functional similarity and Gaussian association spectrum kernel (GAPK) similarity. Then, semantic information from different biological networks is extracted by two GCN modules from different perspectives. Finally, the scores of microbe-disease associations are predicted based on the extracted features. The main innovation of this method lies in the use of two types of information for microbe/disease similarity assessment. Additionally, we extend the disease nodes to address the issue of insufficient features due to low data dimensionality. We optimize the connectivity between the homogeneous entities using random walk with restart (RWR), and then use the optimized similarity matrix as the initial feature matrix. In terms of network understanding, we design a dual branch GCN module, namely GlobalGCN and LocalGCN, to fine-tune node representations by introducing side information, including homologous neighbour nodes. We evaluate the accuracy of the DBGCNMDA model using five-fold cross-validation (5-fold-CV) technique. The results show that the area under the receiver operating characteristic curve (AUC) and area under the precision versus recall curve (AUPR) of the DBGCNMDA model in the 5-fold-CV are 0.9559 and 0.9630, respectively. The results from the case studies using published experimental data confirm a significant number of predicted associations, indicating that DBGCNMDA is an effective tool for predicting potential microbe-disease associations.

Connected interactions: enriching food web research by spatial and social interactions.

This theme issue features 18 papers exploring ecological interactions, encompassing metabolic, social, and spatial connections alongside traditional trophic networks. This integration enriches food web research, offering insights into ecological dynamics. By examining links across organisms, populations, and ecosystems, a hierarchical approach emerges, connecting horizontal effects within organizational levels vertically across biological organization levels. The inclusion of interactions involving humans is a key focus, highlighting the need for their integration into ecology given the complex interactions between human activities and ecological systems in the Anthropocene. The comprehensive exploration in this theme issue sheds light on the interconnectedness of ecological systems and the importance of considering diverse interactions in understanding ecosystem dynamics. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

A spatio-temporal multi-scale fusion algorithm for pine wood nematode disease tree detection

Pine wood nematode infection is a devastating disease. Unmanned aerial vehicle (UAV) remote sensing enables timely and precise monitoring. However, UAV aerial images are challenged by small target size and complex surface backgrounds which hinder their effectiveness in monitoring. To address these challenges, based on the analysis and optimization of UAV remote sensing images, this study developed a spatio-temporal multi-scale fusion algorithm for disease detection. The multi-head, self-attention mechanism is incorporated to address the issue of excessive features generated by complex surface backgrounds in UAV images. This enables adaptive feature control to suppress redundant information and boost the model’s feature extraction capabilities. The SPD-Conv module was introduced to address the problem of loss of small target feature information during feature extraction, enhancing the preservation of key features. Additionally, the gather-and-distribute mechanism was implemented to augment the model’s multi-scale feature fusion capacity, preventing the loss of local details during fusion and enriching small target feature information. This study established a dataset of pine wood nematode disease in the Huangshan area using DJI (DJ-Innovations) UAVs. The results show that the accuracy of the proposed model with spatio-temporal multi-scale fusion reached 78.5%, 6.6% higher than that of the benchmark model. Building upon the timeliness and flexibility of UAV remote sensing, the proposed model effectively addressed the challenges of detecting small and medium-size targets in complex backgrounds, thereby enhancing the detection efficiency for pine wood nematode disease. This facilitates early preemptive preservation of diseased trees, augments the overall monitoring proficiency of pine wood nematode diseases, and supplies technical aid for proficient monitoring.