Modeling and control of freeze-form extrusion fabrication

Freeze-form Extrusion Fabrication (FEF) is an additive manufacturing technique that extrudes ceramic loaded aqueous pastes layer by layer below the paste freezing temperature for component fabrication. As the FEF is aimed at being conducted at low environmental temperatures, down to -20°C, it is necessary to investigate the effect of environmental temperature on the process. The advantages of fabrication at low temperature have been proved by experiments. Comparisons in terms of operation parameters, self-sustaining ability, and system dynamic response were performed at different environmental temperatures ranging from 20°C to -20°C. It is commonly known in paste extrusion processes that due to unmodeled effects such as air bubble release, non-uniform water content, unpredictable agglomerate breakdown, etc., the throughput (extrusion rate) is difficult to control. Moreover, during the extrusion, the rheological characteristics of the paste changes due to liquid migration, resulting in a processing challenge. Because of these difficulties, additional paste extrusion research is still in progress. Traditional PID controllers based on off-line empirical models are inadequate to control the ram extrusion processes. The Recursive Least Square algorithm is used in this research to identify the dynamic responses of the FEF process in real time. An adaptive controller with a novel general tracking control strategy is designed and implemented to regulate the extrusion force in real time. Experimental results demonstrated the robust performance of the controller, allowing the extrusion force to track various types of reference signals, while traditional controllers could only maintain the extrusion force (pressure) at a constant level (operation point). --Abstract, page iv

Adaptive Control of Freeze-Form Extrusion Fabrication Processes

Freeze-form Extrusion Fabrication (FEF) is an additive manufacturing process that extrudes high solids loading aqueous ceramic pastes in a layer-by-layer fashion below the paste freezing temperature for component fabrication. Due to effects such as the air bubble release, agglomerate breakdown, change in paste properties during extrusion as a result of liquid phase migration, etc., the extrusion force is difficult to control. In this paper, an adaptive controller is proposed to regulate the extrusion force. Recursive Least Squares is used to estimate extrusion force model parameters during fabrication and a low-order control scheme capable of tracking general reference trajectories is designed and implemented to regulate the extrusion process. The controller is implemented to regulate the extrusion process. The controller is implemented fro sinusoidal, triangular, and square reference trajectories and the results demonstrate excellent tracking performance of the adaptive extrusion force controller. Several parts were fabricated with the adaptive extrusion force controller. These results illustrate the need for extrusion force control and that variable reference extrusion

Reliable Conflictive Multi-View Learning

Multi-view learning aims to combine multiple features to achieve more comprehensive descriptions of data. Most previous works assume that multiple views are strictly aligned. However, real-world multi-view data may contain low-quality conflictive instances, which show conflictive information in different views. Previous methods for this problem mainly focus on eliminating the conflictive data instances by removing them or replacing conflictive views. Nevertheless, real-world applications usually require making decisions for conflictive instances rather than only eliminating them. To solve this, we point out a new Reliable Conflictive Multi-view Learning (RCML) problem, which requires the model to provide decision results and attached reliabilities for conflictive multi-view data. We develop an Evidential Conflictive Multi-view Learning (ECML) method for this problem. ECML first learns view-specific evidence, which could be termed as the amount of support to each category collected from data. Then, we can construct view-specific opinions consisting of decision results and reliability. In the multi-view fusion stage, we propose a conflictive opinion aggregation strategy and theoretically prove this strategy can exactly model the relation of multi-view common and view-specific reliabilities. Experiments performed on 6 datasets verify the effectiveness of ECML.Comment: 9 pages and to be appeared in AAAI202

A Comprehensive Survey on Database Management System Fuzzing: Techniques, Taxonomy and Experimental Comparison

Database Management System (DBMS) fuzzing is an automated testing technique aimed at detecting errors and vulnerabilities in DBMSs by generating, mutating, and executing test cases. It not only reduces the time and cost of manual testing but also enhances detection coverage, providing valuable assistance in developing commercial DBMSs. Existing fuzzing surveys mainly focus on general-purpose software. However, DBMSs are different from them in terms of internal structure, input/output, and test objectives, requiring specialized fuzzing strategies. Therefore, this paper focuses on DBMS fuzzing and provides a comprehensive review and comparison of the methods in this field. We first introduce the fundamental concepts. Then, we systematically define a general fuzzing procedure and decompose and categorize existing methods. Furthermore, we classify existing methods from the testing objective perspective, covering various components in DBMSs. For representative works, more detailed descriptions are provided to analyze their strengths and limitations. To objectively evaluate the performance of each method, we present an open-source DBMS fuzzing toolkit, OpenDBFuzz. Based on this toolkit, we conduct a detailed experimental comparative analysis of existing methods and finally discuss future research directions.Comment: 34 pages, 22 figure

Current views of drought research: experimental methods, adaptation mechanisms and regulatory strategies

Drought stress is one of the most important abiotic stresses which causes many yield losses every year. This paper presents a comprehensive review of recent advances in international drought research. First, the main types of drought stress and the commonly used drought stress methods in the current experiment were introduced, and the advantages and disadvantages of each method were evaluated. Second, the response of plants to drought stress was reviewed from the aspects of morphology, physiology, biochemistry and molecular progression. Then, the potential methods to improve drought resistance and recent emerging technologies were introduced. Finally, the current research dilemma and future development direction were summarized. In summary, this review provides insights into drought stress research from different perspectives and provides a theoretical reference for scholars engaged in and about to engage in drought research

Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading

Artificial intelligence (AI) holds significant promise in transforming medical imaging, enhancing diagnostics, and refining treatment strategies. However, the reliance on extensive multicenter datasets for training AI models poses challenges due to privacy concerns. Federated learning provides a solution by facilitating collaborative model training across multiple centers without sharing raw data. This study introduces a federated attention-consistent learning (FACL) framework to address challenges associated with large-scale pathological images and data heterogeneity. FACL enhances model generalization by maximizing attention consistency between local clients and the server model. To ensure privacy and validate robustness, we incorporated differential privacy by introducing noise during parameter transfer. We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19,461 whole-slide images of prostate cancer from multiple centers. In the diagnosis task, FACL achieved an area under the curve (AUC) of 0.9718, outperforming seven centers with an average AUC of 0.9499 when categories are relatively balanced. For the Gleason grading task, FACL attained a Kappa score of 0.8463, surpassing the average Kappa score of 0.7379 from six centers. In conclusion, FACL offers a robust, accurate, and cost-effective AI training model for prostate cancer pathology while maintaining effective data safeguards.Comment: 14 page

Methylene Blue Attenuates Lung Injury Induced by Hindlimb Ischemia Reperfusion in Rats

Objective. This study was aimed to investigate the protective effect of methylene blue against lung injury induced by reperfusion of ischemic hindlimb in a rat model. Methods. Twenty-four healthy adult male Sprague-Dawley rats were equally randomized into three groups: sham (SM) group, ischemia reperfusion (IR) group, and methylene blue (MB) group. Rats in both IR and MB groups were subjected to 4 h of ischemia by clamping the left femoral artery and then followed by 4 h of reperfusion. Treatment with 1% methylene blue (50 mg/kg) was administrated intraperitoneally at 10 min prior to reperfusion in the MB group. After 4 h of reperfusion, malondialdehyde (MDA) level, myeloperoxidase (MPO), and superoxide dismutase (SOD) activities in lung tissue were detected; inflammatory cytokines, including IL-1β and IL-6, were measured in bronchoalveolar lavage fluid (BALF); correspondingly, the morphological changes and water content in both gastrocnemius muscle and lung samples were evaluated. Results. Hindlimb IR caused remarkable morphological abnormalities and edema in both muscle and lung tissues. SOD activity was decreased, both the MPO activity and MDA level in lung tissue, as well as IL-1β and IL-6 levels in BALF, were increased in the IR group (p<0.05). Compared with the IR group, SOD activity was increased, whereas MPO activity and MDA level in lung tissue and IL-1β and IL-6 levels in BALF were decreased in the MB group (p<0.05). Also, the histological damage and edema in both lung and muscle tissues were significantly attenuated by the treatment of methylene blue. Conclusion. Methylene blue attenuates lung injury induced by hindlimb IR in rats, at least in part, by inhibiting oxidative stress

Combination of Clostridium butyricum and Corn Bran Optimized Intestinal Microbial Fermentation Using a Weaned Pig Model

Experimental manipulation of the intestinal microbiota influences health of the host and is a common application for synbiotics. Here Clostridium butyricum (C. butyricum, C.B) combined with corn bran (C.B + Bran) was taken as the synbiotics application in a waned pig model to investigate its regulation of intestinal health over 28 days postweaning. Growth performance, fecal short chain fatty acids (SCFAs) and bacterial community were evaluated at day 14 and day 28 of the trial. Although the C.B + Bran treatment has no significant effects on growth performance (P &gt; 0.05), it optimized the composition of intestinal bacteria, mainly represented by increased acetate-producing bacteria and decreased pathogens. Microbial fermentation in the intestine showed a shift from low acetate and isovalerate production on day 14 to enhanced acetate production on day 28 in the C.B + Bran treatment. Thus, C.B and corn bran promoted intestinal microbial fermentation and optimized the microbial community for pigs at an early age. These findings provide perspectives on the advantages of synbiotics as a new approach for effective utilization of corn barn

Moderate Dietary Protein Restriction Optimized Gut Microbiota and Mucosal Barrier in Growing Pig Model

Appropriate protein concentration is essential for animal at certain stage. This study evaluated the effects of different percentages of dietary protein restriction on intestinal health of growing pigs. Eighteen barrows were randomly assigned to a normal (18%), low (15%), and extremely low (12%) dietary protein concentration group for 30 days. Intestinal morphology and permeability, bacterial communities, expressions, and distributions of intestinal tight junction proteins, expressions of biomarkers of intestinal stem cells (ISCs) and chymous bacterial metabolites in ileum and colon were detected. The richness and diversity of bacterial community analysis with Chao and Shannon index were highest in the ileum of the 15% crude protein (CP) group. Ileal abundances of Streptococcaceae and Enterobacteriaceae decreased respectively, while beneficial Lactobacillaceae, Clostridiaceae_1, Actinomycetaceae, and Micrococcaceae increased their proportions with a protein reduction of 3 percentage points. Colonic abundances of Ruminococcaceae, Christensenellaceae, Clostridiaceae_1, Spirochaetaceae, and Bacterodales_S24-7_group declined respectively, while proportions of Lachnospiraceae, Prevotellaceae, and Veillonellaceae increased with dietary protein reduction. Concentrations of most bacterial metabolites decreased with decreasing dietary protein concentration. Ileal barrier function reflected by expressions of tight junction proteins (occludin, zo-3, claudin-3, and claudin-7) did not show significant decrease in the 15% CP group while sharply reduced in the 12% CP group compared to that in the 18% CP group. And in the 15% CP group, ileal distribution of claudin-3 mainly located in the cell membrane with complete morphological structure. In low-protein treatments, developments of intestinal villi and crypts were insufficient. The intestinal permeability reflected by serous lipopolysaccharide (LPS) kept stable in the 15% CP group while increased significantly in the 12% CP group. The expression of ISCs marked by Lgr5 slightly increased in ileum of the 15% CP group. Colonic expressions of tight junction proteins declined in extremely low protein levels. In conclusion, moderate protein restriction (15% CP) can optimize the ileal microbiota structure via strengthening beneficial microbial populations and suppressing harmful bacterial growth and altering the function of ileal tight junction proteins as well as epithelial cell proliferation

Chinese university students’ preferences for physical activity incentive programs: a discrete choice experiment

PurposeThis study aims to explore and compare Chinese university students’ preferences for various physical activity motivation programs.Patients and methodsA cross-sectional study was conducted in China from February 25 to March 25, 2022. Participants anonymously completed an online questionnaire based on a DCE. A total of 1,358 university students participated in the survey. The conditional logit model (CLM), willingness to accept (WTA), and propensity score matching (PSM) were used to assess college students’ preferences for different attributes and levels of physical activity incentive programs.ResultsRespondents identified the number of bonus, exercise time, and academic rewards as the three most significant attributes of the athletic incentive program. The importance of each attribute varied based on individual characteristics such as gender and BMI. In CLM, college students displayed a preference for a “¥4” bonus amount (OR: 2.04, 95% CI 1.95–2.13), “20 min” of exercise time (OR: 1.85, 95% CI 1.79–1.92), and “bonus points for comprehensive test scores” as academic rewards (OR: 1.33, 95% CI 1.28–1.37). According to the WTA results, college students were willing to accept the highest cost to obtain academic rewards tied to composite test scores.ConclusionThe number of bonus, exercise time, and academic rewards emerge as the three most crucial attributes of physical activity incentive programs. Furthermore, college students with different characteristics exhibit heterogeneity in their preferences for such programs. These findings can guide the development of programs and policies aimed at motivating college students to engage in physical activities