Analyzing the Potential of Using Social Robots in Autism Classroom Settings

In recent years, social robots have rapidly advanced alongside the progress of artificial intelligence. Countries around the world have been enacting strategic initiatives that combine robotics and artificial intelligence, leading to an increasing exploration of the application of AI technology in the field of education. In the context of autism intervention, social robots have shown promising results in intervention programs and behavior therapy for children with autism. However, there is a lack of research specifically focusing on the use of social robots in autism classroom settings. Therefore, we have synthesized existing studies and proposed the integration of social robots into autism classrooms. Through the collaboration between robots and teachers, as well as the interaction between robots and students, we aim to enhance the attention of children with autism in the classroom and explore new impacts on their classroom performance, knowledge acquisition, and generalization of after-class skills

Person re-identification in the real scene based on the deep learning

Person re-identification aims at automatically retrieving a person of interest across multiple non-overlapping camera views. Because of increasing demand for real-world applications in intelligent video surveillance, person re-identification has become an important computer vision task and achieved high performance in recent years. However, the traditional person re-identification research mainly focus on matching cropped pedestrian images between queries and candidates on commonly used datasets and divided into two steps: pedestrian detection and person re-identification, there is still a big gap with practical applications. Under the premise of model optimization, based on the existing object detection and person re-identification, this paper achieves a one-step search of the specific pedestrians in the whole images or video sequences in the real scene. The experimental results show that our method is effective in commonly used datasets and has achieved good results in real-world applications, such as finding criminals, cross-camera person tracking, and activity analysis.26th International Symposium on Artificial Life and Robotics, AROB 26th 2021, January 21–23, 2021, Beppu, Japan and Onlin

Surprising Performances of Students with Autism in Classroom with NAO Robot

Autism is a developmental disorder that manifests in early childhood and persists throughout life, profoundly affecting social behavior and hindering the acquisition of learning and social skills in those diagnosed. As technological advancements progress, an increasing array of technologies is being utilized to support the education of students with Autism Spectrum Disorder (ASD), aiming to improve their educational outcomes and social capabilities. Numerous studies on autism intervention have highlighted the effectiveness of social robots in behavioral treatments. However, research on the integration of social robots into classroom settings for children with autism remains sparse. This paper describes the design and implementation of a group experiment in a collective classroom setting mediated by the NAO robot. The experiment involved special education teachers and the NAO robot collaboratively conducting classroom activities, aiming to foster a dynamic learning environment through interactions among teachers, the robot, and students. Conducted in a special education school, this experiment served as a foundational study in anticipation of extended robot-assisted classroom sessions. Data from the experiment suggest that ASD students in classrooms equipped with the NAO robot exhibited notably better performance compared to those in regular classrooms. The humanoid features and body language of the NAO robot captivated the students\u27 attention, particularly during talent shows and command tasks, where students demonstrated heightened engagement and a decrease in stereotypical repetitive behaviors and irrelevant minor movements commonly observed in regular settings. Our preliminary findings indicate that the NAO robot significantly enhances focus and classroom engagement among students with ASD, potentially improving educational performance and fostering better social behaviors

Revealing the role of regulatory T cells in the tumor microenvironment of lung adenocarcinoma: a novel prognostic and immunotherapeutic signature

BackgroundRegulatory T cells (Tregs), are a key class of cell types in the immune system. In the tumor microenvironment (TME), the presence of Tregs has important implications for immune response and tumor development. Relatively little is known about the role of Tregs in lung adenocarcinoma (LUAD).MethodsTregs were identified using but single-cell RNA sequencing (scRNA-seq) analysis and interactions between Tregs and other cells in the TME were investigated. Next, we used multiple bulk RNA-seq datasets to construct risk models based on marker genes of Tregs and explored differences in prognosis, mutational landscape, immune cell infiltration and immunotherapy between high- and low-risk groups, and finally, qRT-PCR and cell function experiments were performed to validate the model genes.ResultsThe cellchat analysis showed that MIF-(CD74+CXCR4) pairs play a key role in the interaction of Tregs with other cell subpopulations, and the Tregs-associated signatures (TRAS) could well classify multiple LUAD cohorts into high- and low-risk groups. Immunotherapy may offer greater potential benefits to the low-risk group, as indicated by their superior survival, increased infiltration of immune cells, and heightened expression of immune checkpoints. Finally, the experiment verified that the model genes LTB and PTTG1 were relatively highly expressed in cancer tissues, while PTPRC was relatively highly expressed in paracancerous tissues. Colony Formation assay confirmed that knockdown of PTTG1 reduced the proliferation ability of LUAD cellsConclusionTRAS were constructed using scRNA-seq and bulk RNA-seq to distinguish patient risk subgroups, which may provide assistance in the clinical management of LUAD patients

Advanced age is associated with increased adverse outcomes in patients undergoing middle cerebral artery stenting

PurposeThis study tried to evaluate whether advanced age has an increased incidence of major complications in patients undergoing MCA stenting.MethodsA total of 348 patients who underwent MCA stenting were reviewed from a prospectively maintained database. Ninety-day ischemic stroke, intracerebral hemorrhage, and death outcomes were compared among the young (≤40 years old), middle (41–60 years old) and old (≥61 years old) groups. Univariate analysis and multivariable logistic regression analysis were used to investigate different variables associated with 90-day major adverse events. Kaplan–Meier analysis was performed to determine long-term outcomes during follow-up.ResultsThe incidence of 90-day ischemic stroke was 9.26% in the old group, 2.86% in the middle group, and 0% in the young group (P = 0.024). The incidence of all 90-day major adverse events was 3.33% in patients ≤40 years old, 19.90% in patients 41–60 years old, and 24.07% in patients ≥61 years old, with statistical significance (P = 0.04). Advanced age was associated with increased 90-day ischemic stroke (OR = 1.074, 95% CI: 1.019–1.132, P = 0.007; adjusted OR: 1.071, 95% CI: 1.008–1.138, P = 0.026) and 90-day death (OR = 1.072, 95% CI: 1.012–1.135, P = 0.018; adjusted OR: 1.095, 95% CI: 1.015–1.182, P = 0.018). Meanwhile, advanced age was also associated with decreased long-term survival and ischemic stroke-free survival during follow-up.ConclusionOur data indicated that MCA stenting in elderly patients is associated with a high risk of adverse events and should be cautiously considered

Clinical pathological characteristics of breast cancer patients with secondary diabetes after systemic therapy: a retrospective multicenter study

The objective of this study was to investigate the clinical pathological characteristics of breast cancer (BC) patients with secondary diabetes after systemic therapy without preexisting diabetes. A total of 1434 BC patients received systemic therapy and were analyzed retrospectively. Fasting plasma glucose (FPG) levels were monitored prior to the treatments, during the course of systemic therapy, and at the follow-up visits. Cox regression models were used to estimate the associations between the clinical pathological characteristics of BC and the cause-specific hazard of developing secondary diabetes. Among the 1434 BC patients, 151 had preexisting type 2 diabetes. Of the remaining 1283 patients with normal FPG levels prior to the systemic therapy, 59 developed secondary diabetes and 72 displayed secondary impaired fasting glucose (IFG) over a mean follow-up of 41 months. The prevalence of secondary type 2 diabetes in BC patients was 4.6 % (59/1283), which was obviously higher than that of the normal control group (1.4 %, P < 0.001). The percentage of older patients (P < 0.05), menopausal patients (P < 0.001), and obese patients (P < 0.01) tended to be lower in the secondary diabetic group. In addition, these patients with secondary diabetes had later pathological stages (P < 0.01), more lymph node metastasis (P < 0.05), negative estrogen receptor (ER) expression (P < 0.05), and smaller size of tumors (P < 0.05). After adjusting for age and BMI, the risk of developing secondary diabetes and IFG in subjects with later pathological stage BC (hazard ratio (HR) = 1.623; 95 % confidence interval (CI) 1.128–2.335 (P < 0.01)), negative progesterone receptor (PR) expression (HR = 0.530; 95 % CI 0.372–0.755 (P < 0.001)), positive human epidermal growth factor receptor 2 (HER2) expression (HR = 1.822; 95 % CI 1.230–2.700 (P < 0.01)), and more lymph node metastasis (HR = 1.595; 95 % CI 1.128–2.258 (P < 0.01)) was significantly higher. In conclusion, this study shows that an increase in the incidence of diabetes among breast cancer survivors after systemic therapy, especially the patients with later pathological stages, more lymph node metastasis, negative hormone receptor expression, and positive HER2 expression. Our study suggests that greater diabetes screening and prevention strategies among breast cancer patients after systemic treatment are needed in China

Text Sentiment Analysis Based on Transformer and Augmentation

With the development of Internet technology, social media platforms have become an indispensable part of people’s lives, and social media have been integrated into people’s life, study, and work. On various forums, such as Taobao and Weibo, a large number of people’s footprints are left all the time. It is these chats, comments, and other remarks with people’s emotional evaluations that make up part of public opinion. Analysis of this network public opinion is conducive to maintaining the peaceful development of society. Therefore, sentiment analysis has become a hot research field and has made great strides as one of the hot topics in the field of natural language processing. Currently, the BERT model and its variants have achieved excellent results in the field of NLP. However, these models cannot be widely used due to huge demands on computing resources. Therefore, this paper proposes a model based on the transformer mechanism, which mainly includes two parts: knowledge distillation and text augmentation. The former is mainly used to reduce the number of parameters of the model, reducing the computational cost and training time of the model, and the latter is mainly used to expand the task text so that the model can achieve excellent results in the few-sample sentiment analysis task. Experiments show that our model achieves competitive results.</jats:p

Analyzing the Potential of Using Social Robots in Autism Classroom Settings

In recent years, social robots have rapidly advanced alongside the progress of artificial intelligence. Countries around the world have been enacting strategic initiatives that combine robotics and artificial intelligence, leading to an increasing exploration of the application of AI technology in the field of education. In the context of autism intervention, social robots have shown promising results in intervention programs and behavior therapy for children with autism. However, there is a lack of research specifically focusing on the use of social robots in autism classroom settings. Therefore, we have synthesized existing studies and proposed the integration of social robots into autism classrooms. Through the collaboration between robots and teachers, as well as the interaction between robots and students, we aim to enhance the attention of children with autism in the classroom and explore new impacts on their classroom performance, knowledge acquisition, and generalization of after-class skills