Investigation of the Effects of Heating & Quenching on the Mechanical Properties and Microstructure of 22MnB5 Steel Sheets

Hot stamping of high-strength structural automotive components with tailored mechanical properties will help to reduce vehicle weight as well as improve crashworthiness. The purpose of this research is to establish the relationship between the quenching start temperature in a hot stamping process and the as-quenched mechanical properties and microstructures. A series of heating and quenching trials were carried out on 22MnB5 steel sheets having 2 different thicknesses, and final mechanical properties were determined from tensile tests and corresponding microstructures were analyzed. It was found that when the quenching start temperature is decreased to between 900°C and 720°C, the final strength of the as-quenched steel will rapidly decrease from about 1500 MPa to less than 570 MPa. The results of this research can be used to design structural automotive parts with tailored properties

Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception

Personalized chatbot-based teaching assistants can be crucial in addressing increasing classroom sizes, especially where direct teacher presence is limited. Large language models (LLMs) offer a promising avenue, with increasing research exploring their educational utility. However, the challenge lies not only in establishing the efficacy of LLMs but also in discerning the nuances of interaction between learners and these models, which impact learners' engagement and results. We conducted a formative study in an undergraduate computer science classroom (N=145) and a controlled experiment on Prolific (N=356) to explore the impact of four pedagogically informed guidance strategies and the interaction between student approaches and LLM responses. Direct LLM answers marginally improved performance, while refining student solutions fostered trust. Our findings suggest a nuanced relationship between the guidance provided and LLM's role in either answering or refining student input. Based on our findings, we provide design recommendations for optimizing learner-LLM interactions

Exploring The Design of Prompts For Applying GPT-3 based Chatbots: A Mental Wellbeing Case Study on Mechanical Turk

Large-Language Models like GPT-3 have the potential to enable HCI designers and researchers to create more human-like and helpful chatbots for specific applications. But evaluating the feasibility of these chatbots and designing prompts that optimize GPT-3 for a specific task is challenging. We present a case study in tackling these questions, applying GPT-3 to a brief 5-minute chatbot that anyone can talk to better manage their mood. We report a randomized factorial experiment with 945 participants on Mechanical Turk that tests three dimensions of prompt design to initialize the chatbot (identity, intent, and behaviour), and present both quantitative and qualitative analyses of conversations and user perceptions of the chatbot. We hope other HCI designers and researchers can build on this case study, for other applications of GPT-3 based chatbots to specific tasks, and build on and extend the methods we use for prompt design, and evaluation of the prompt design

CD8+ T cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma

IntroductionMounting evidence has revealed that the interactions and dynamic alterations among immune cells are critical in shaping the tumor microenvironment and ultimately map onto heterogeneous clinical outcomes. Currently, the underlying clinical significance of immune cell evolutions remains largely unexplored in hepatocellular carcinoma (HCC).MethodsA total of 3,817 immune cells and 1,750 HCC patients of 15 independent public datasets were retrieved. The Seurat and Monocle algorithms were used to depict T cell evolution, and nonnegative matrix factorization (NMF) was further applied to identify the molecular classification. Subsequently, the prognosis, biological characteristics, genomic variations, and immune landscape among distinct clusters were decoded. The clinical efficacy of multiple treatment approaches was further investigated.ResultsAccording to trajectory gene expression, three heterogeneous clusters with different clinical outcomes were identified. C2, with a more advanced pathological stage, presented the most dismal prognosis relative to C1 and C3. Eight independent external cohorts validated the robustness and reproducibility of the three clusters. Further explorations elucidated C1 to be characterized as lipid metabolic HCC, and C2 was referred to as cell-proliferative HCC, whereas C3 was defined as immune inflammatory HCC. Moreover, C2 also displayed the most conspicuous genomic instability, and C3 was deemed as “immune-hot”, having abundant immune cells and an elevated expression of immune checkpoints. The assessments of therapeutic intervention suggested that patients in C1 were suitable for transcatheter arterial chemoembolization treatment, and patients in C2 were sensitive to tyrosine kinase inhibitors, while patients in C3 were more responsive to immunotherapy. We also identified numerous underlying therapeutic agents, which might be conducive to clinical transformation in the future.ConclusionsOur study developed three clusters with distinct characteristics based on immune cell evolutions. For specifically stratified patients, we proposed individualized treatment strategies to improve the clinical outcomes and facilitate the clinical management

Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health

Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health non-profit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains

Epidermal growth factor induces HCCR expression via PI3K/Akt/mTOR signaling in PANC-1 pancreatic cancer cells

<p>Abstract</p> <p>Background</p> <p>Human cervical cancer oncoprotein 1 (HCCR-1), reported as a negative regulator of p53, is over-expressed in a variety of human cancers. However, it is yet unknown whether HCCR-1 plays any role in pancreatic cancer development. The aim of this study was to investigate the effect of epidermal growth factor on the expression of HCCR in pancreatic cancer cells, and to explore if PI3K/Akt/mTOR signaling pathway mediated this expression.</p> <p>Methods</p> <p>A polyclonal antibody against HCCR protein was raised by immunizing Balb/c mice with the purified recombinant protein pMBPc-HCCR. Tissue samples were constructed on a tissue chip, and the expression of HCCR was investigated by immunohistochemistry assay and Western blotting. Pancreatic cell line, PANC-1 cells were stably transfected with plasmids containing sense-HCCR-1 fragment and HCCR siRNA fragment. MTT and transwell assay were used to investigate the proliferation and invasion of stable tansfectants. The specific inhibitor of PI3K and mTOR was used to see if PI3K/mTOR signal transduction was involved in the induction of HCCR gene expression. A Luciferase assay was used to see if Akt can enhance the HCCR promoter activity.</p> <p>Results</p> <p>HCCR was up-regulated in pancreatic tumor tissues (mean Allred score 4.51 ± 1.549 <it>vs</it>. 2.87 ± 2.193, P < 0.01), especially with high expression in poorly differentiated pancreatic cancer. The growth of cells decreased in HCCR-1 siRNA transfected cells compared with vector transfectants. The number of invasion cells was significantly lower in HCCR-1 siRNA transfected cells (24.4 ± 9.9) than that in vector transfectants (49.1 ± 15.4). Treatment of PANC-1 cells with epidermal growth factor increased HCCR protein level in a dose- and time-dependent manner. However, application of LY294002 and rapamycin caused a dramatic reduction of epidermal growth factor-induced HCCR expression. Over-expression of exogenous constitutively active Akt increased the HCCR promoter activity; in contrast, dominant negative Akt decreased the promoter activity.</p> <p>Conclusions</p> <p>EGF-induced HCCR-1 over-expression is mediated by PI3K/AKT/mTOR signaling which plays a pivotal role in pancreatic tumor progression, suggesting that HCCR-1 could be a potential target for cancer therapeutics.</p

Research on Path Planning of Mobile Robot Based on Improved Theta* Algorithm

The Theta* algorithm is a path planning algorithm based on graph search, which gives the optimal path with more flexibility than A* algorithm in terms of routes. The traditional Theta* algorithm is difficult to take into account with the global and details in path planning and traverses more nodes, which leads to a large amount of computation and is not suitable for path planning in large scenarios directly by the Theta* algorithm. To address this problem, this paper proposes an improved Theta* algorithm, namely the W-Theta* algorithm. The heuristic function of Theta* is improved by introducing a weighting strategy, while the default Euclidean distance calculation formula of Theta* is changed to a diagonal distance calculation formula, which finally achieves a reduction in computation time while ensuring a shorter global path; the trajectory optimization is achieved by curve fitting of the generated path points to make the motion trajectory of the mobile robot smoother. Simulation results show that the improved algorithm can quickly plan paths in large scenarios. Compared with other path planning algorithms, the algorithm has better performance in terms of time and computational cost. In different scenarios, the W-Theta* algorithm reduces the computation time of path planning by 81.65% compared with the Theta* algorithm and 79.59% compared with the A* algorithm; the W-Theta* algorithm reduces the memory occupation during computation by 44.31% compared with the Theta* algorithm and 29.33% compared with the A* algorithm

Effects of Arbuscular Mycorrhizal Fungi on Leaf N: P: K Stoichiometry in Agroecosystem

Leaf nitrogen (N), phosphorus (P), and potassium (K) stoichiometry can reflect plant strategies of nutrient allocation, which play key roles in ensuring food security and maintaining nutrient balance in the agroecosystem. Arbuscular mycorrhizal fungi (AMF) inoculation is an effective and green management measure affecting nutrient uptake and utilization strategies, especially in the agroecosystem. However, the interplay between AMF and leaf nutrient stoichiometry that is important for sustainable agriculture remain underexplored. Therefore, the efficacy of AMF in improving leaf nutrients of host plants in agricultural ecosystems were tested with meta-analysis by 1932 pairs of observations in research publications from 1995 to 2022. Overall analysis showed that AMF inoculation increases leaf N, P, and K by 8.75%, 24.61%, and 13.54%, respectively. Moreover, leaf P: K increased by 11.74% by AMF inocula, but leaf N: P and N: K of host plants decreased by 15.38% and 5.52%, respectively. Furthermore, the AMF effect on leaf nutrient stoichiometry was significantly regulated by species, life cycle, and growth habits of host plants. The prominent efficacy of AMF was higher for leaf P in fruit (30.06%), perennial (30.19%), and woody plants (31.6%) than other groups. Moreover, AMF effects on leaf N: P: K stoichiometry of inoculated crops varied depending on the identity of AMF. The Glomeraceae (especially Rhizophagus genera) increased more leaf P content than other AMF families. Thus, the leaf nutrient of host plants significantly increased by AMF inocula, especially leaf P content in the agroecosystem. The effect of AMF on leaf N: P: K stoichiometry was related to plant species, plant life cycle, plant growth habits, and the identity of AMF. These findings highlight the response of AMF to the strategies of nutrient in host plants and provide a theoretical and applicable way for better crop yield and sustainable agriculture

Biodiversity and Variations of Arbuscular Mycorrhizal Fungi Associated with Roots along Elevations in Mt. Taibai of China

(1) Background: environmental gradient strongly affects microbial biodiversity, but which factors drive the diversity of arbuscular mycorrhizal fungi (AMF) associated with roots at relatively large spatial scales requires further research; (2) Methods: an experiment on large spatial scales of Mt. Taibai was conducted to explore the biodiversity and drivers of AMF-associated with roots using high-throughput sequencing; (3) Results: a total of 287 operational taxonomic units (OTUs) belong to 62 species representing 4 identified and 1 unclassified order were identified along different altitudinal gradients. With increasing altitude, AMF colonization could be simulated by a quadratic function trend, and altitude has a significant impact on colonization. AMF alpha diversity, including the Sobs and Shannon indexes, tended to be quadratic function trends with increasing altitude. The highest diversity indices occurred at mid-altitudes, and altitude had a significant effect on them. AMF communities have different affinities with soil and root nutrient, and Glomus is most affected by soil and root nutrient factors through the analysis of the heatmap. Glomus are the most dominant, with an occurrence frequency of 91.67% and a relative abundance of 61.29% and 53.58% at the level of species and OTU, respectively. Furthermore, AMF diversity were mostly associated with soil and root nutrients; (4) Conclusions: in general, AMF molecular diversity is abundant in Mt. Taibai, and altitude and nutrient properties of soil and root are the main influencing factors on AMF diversity and distribution

Gradient Corner Pooling for Keypoint-Based Object Detection

Detecting objects as multiple keypoints is an important approach in the anchor-free object detection methods while corner pooling is an effective feature encoding method for corner positioning. The corners of the bounding box are located by summing the feature maps which are max-pooled in the x and y directions respectively by corner pooling. In the unidirectional max pooling operation, the features of the densely arranged objects of the same class are prone to occlusion. To this end, we propose a method named Gradient Corner Pooling. The spatial distance information of objects on the feature map is encoded during the unidirectional pooling process, which effectively alleviates the occlusion of the homogeneous object features. Further, the computational complexity of gradient corner pooling is the same as traditional corner pooling and hence it can be implemented efficiently. Gradient corner pooling obtains consistent improvements for various keypoint-based methods by directly replacing corner pooling. We verify the gradient corner pooling algorithm on the dataset and in real scenarios, respectively. The networks with gradient corner pooling located the corner points earlier in the training process and achieve an average accuracy improvement of 0.2%-1.6% on the MS-COCO dataset. The detectors with gradient corner pooling show better angle adaptability for arrayed objects in the actual scene test