Harnessing multi-domain knowledge for user-centric product conceptual design

Conceptual design is the design phase that deploys product functions and structures based on user requirements and ultimately generates conceptual design solutions. The increasing diversification of products has led to the promotion of customized design that involves deep user participation. As a result, there has been a growing focus on user-centric conceptual design. In this regard, the relationship among users, designers, and design solutions has been changed, which has brought challenges to the traditional designer-oriented design model. To address the complex understanding and decision-making problem caused by deeper user participation, emerging new user-centric product conceptual design model needs to be discussed. In the new design model, addressing the changing or growing requirements of users through the design of solutions and leveraging multi-domain knowledge to guide the conceptual design process are the critical areas of focus. To further describe this design model, this paper examines the user-centric interconnection among users, designers, design solutions, and multi-domain knowledge. In order to optimize design solutions, the solution resolution process and knowledge mapping based on design deviations are considered effective approaches. In addition, the paper also presents the types of design deviations and the multi-domain knowledge support techniques

Survival outcomes of stage I colorectal cancer:development and validation of the ACEPLY model using two prospective cohorts

BACKGROUND: Approximately 10% of stage I colorectal cancer (CRC) patients experience unfavorable clinical outcomes after surgery. However, little is known about the subset of stage I patients who are predisposed to high risk of recurrence or death. Previous evidence was limited by small sample sizes and lack of validation. METHODS: We aimed to identify early indicators and develop a risk stratification model to inform prognosis of stage I patients by employing two large prospective cohorts. Prognostic factors for stage II tumors, including T stage, number of nodes examined, preoperative carcinoma embryonic antigen (CEA), lymphovascular invasion, perineural invasion (PNI), and tumor grade were investigated in the discovery cohort, and significant findings were further validated in the other cohort. We adopted disease-free survival (DFS) as the primary outcome for maximum statistical power and recurrence rate and overall survival (OS) as secondary outcomes. Hazard ratios (HRs) were estimated from Cox proportional hazard models, which were subsequently utilized to develop a multivariable model to predict DFS. Predictive performance was assessed in relation to discrimination, calibration and net benefit. RESULTS: A total of 728 and 413 patients were included for discovery and validation. Overall, 6.7% and 4.1% of the patients developed recurrences during follow-up. We identified consistent significant effects of PNI and higher preoperative CEA on inferior DFS in both the discovery (PNI: HR = 4.26, 95% CI: 1.70–10.67, p = 0.002; CEA: HR = 1.46, 95% CI: 1.13–1.87, p = 0.003) and the validation analysis (PNI: HR = 3.31, 95% CI: 1.01–10.89, p = 0.049; CEA: HR = 1.58, 95% CI: 1.10–2.28, p = 0.014). They were also significantly associated with recurrence rate. Age at diagnosis was a prominent determinant of OS. A prediction model on DFS using Age at diagnosis, CEA, PNI, and number of LYmph nodes examined (ACEPLY) showed significant discriminative performance (C-index: 0.69, 95% CI:0.60–0.77) in the external validation cohort. Decision curve analysis demonstrated added clinical benefit of applying the model for risk stratification. CONCLUSIONS: PNI and preoperative CEA are useful indicators for inferior survival outcomes of stage I CRC. Identification of stage I patients at high risk of recurrence is feasible using the ACEPLY model, although the predictive performance is yet to be improved. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02693-7

Thermal Conductivity of PNIPAm Hydrogels and Heat Management as Smart Windows

Abstract Though thermoresponsive lower critical solution temperature (LCST) hydrogels have attracted intense attention to be applied in smart windows, less efforts are paid on the LCST effect on the heat transfer process. Herein, the research mainly focuses on heat transfer process of thermoresponsive PNIPAm hydrogels. It is the first time reported that LCST behaviors can decrease thermal conductivity upon heating process. To be utilized as a smart window, thermal conduction flux is investigated yet the thermal conduction energy occupies little to manage the thermal transfer process in a model house. It is found that radiation thermal transfer predominates the heat transfer process for PNIPAm‐based smart windows. These results are meaningful to provide basic data for energy transfer in using thermoresponsive hydrogels

A smart conflict resolution model using multi-layer knowledge graph for conceptual design

Reducing the impact of conflicts on requirement-function-structure mapping in the early stage of product design is an important measure to achieve conceptual innovation, which relies on accurate reasoning of multi-domain knowledge. As product requirements become more personalized and diverse, traditional discrete knowledge organization and reasoning methods are difficult to adapt to the challenges of continuity and precision in conceptual solution. Knowledge graphs with complex networks have obvious advantages in association detection, knowledge visualization, and explainable reasoning of implicit knowledge, which offer innovative opportunities for conflict resolution in conceptual design. Therefore, a smart conflict resolution model using a multi-layer Knowledge Graph for Conceptual Design(mKGCD) is proposed in this study. A knowledge expression form of FBS-oriented design patent vocabulary is proposed, which is used for knowledge entity recognition and relation extraction based on natural language processing. A label mapping method based on inventive principles is used for patent classification and a four-layer semantic network for conflict resolution is constructed. Through semantic distance calculation, the designer's requirements for function/behavior/structure are smart deployed to obtain appropriate knowledge. A case study of the conceptual design of a collapsible installation and handling equipment demonstrates the feasibility of the proposed approach. The proposed method can not only meet the functional solution and innovation in the context of different design requirements, but also effectively improve the design efficiency in the iterative design process by means of multiple meanings of one graph

Forest Fire Spread Monitoring and Vegetation Dynamics Detection Based on Multi-Source Remote Sensing Images

With the increasingly severe damage wreaked by forest fires, their scientific and effective prevention and control has attracted the attention of countries worldwide. The breakthrough of remote sensing technologies implemented in the monitoring of fire spread and early warning has become the development direction for their prevention and control. However, a single remote sensing data collection point cannot simultaneously meet the temporal and spatial resolution requirements of fire spread monitoring. This can significantly affect the efficiency and timeliness of fire spread monitoring. This article focuses on the mountain fires that occurred in Muli County, on 28 March 2020, and in Jingjiu Township on 30 March 2020, in Liangshan Prefecture, Sichuan Province, as its research objects. Multi-source satellite remote sensing image data from Planet, Sentinel-2, MODIS, GF-1, GF-4, and Landsat-8 were used for fire monitoring. The spread of the fire time series was effectively and quickly obtained using the remote sensing data at various times. Fireline information and fire severity were extracted based on the calculated differenced normalized burn ratio (dNBR). This study collected the meteorological, terrain, combustibles, and human factors related to the fire. The random forest algorithm analyzed the collected data and identified the main factors, with their order of importance, that affected the spread of the two selected forest fires in Sichuan Province. Finally, the vegetation coverage before and after the fire was calculated, and the relationship between the vegetation coverage and the fire severity was analyzed. The results showed that the multi-source satellite remote sensing images can be utilized and implemented for time-evolving forest fires, enabling forest managers and firefighting agencies to plan improved firefighting actions in a timely manner and increase the effectiveness of firefighting strategies. For the forest fires in Sichuan Province studied here, the meteorological factors had the most significant impact on their spread compared with other forest fire factors. Among all variables, relative humidity was the most crucial factor affecting the spread of forest fires. The linear regression results showed that the vegetation coverage and dNBR were significantly correlated before and after the fire. The vegetation coverage recovery effects were different in the fire burned areas depending on fire severity. High vegetation recovery was associated with low-intensity burned areas. By combining the remote sensing data obtained by multi-source remote sensing satellites, accurate and macro dynamic monitoring and quantitative analysis of wildfires can be carried out. The study’s results provide effective information on the fires in Sichuan Province and can be used as a technical reference for fire spread monitoring and analysis through remote sensing, enabling accelerated emergency responses

Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China

Although low-intensity forest fires are a necessary part of healthy echo system, high-intensity forest fires continue to affect the diversity of forest ecosystems and species. Therefore, it is necessary to study the driving factors of forest fires and explore their possible locations and probabilities in complex forest terrain. In this article, we determined the relative influences of different types of factors on the occurrence of forest fires in Heilongjiang forest areas and compared the performance of artificial neural networks and logistic regression for wildfire prediction. By analyzing Heilongjiang forest fire data from 2002 to 2015 and constructing a model, we found that climate factors, topographical factors, and vegetation type factors play a crucial role in Heilongjiang’s wildfires. During the fire prevention period, temperature and wind speed have a more significant influence than other factors. According to the variable screening that we conducted, the model built by the variables that we used can predict 84% of forest fires in Heilongjiang Province. For recent wildfires (2019–2020) in most areas, we can use artificial neural networks for relatively accurate verification (85.2%). Therefore, artificial neural networks are very suitable for the prediction of forest fires in Heilongjiang Province. Through the prediction results, we also created a probability distribution map of fire occurrence in the study area. On this basis, we also analyzed the changes in the probability of natural fires under the weather changing trend, which can effectively aid in fire prevention and extinguishment

Analysis of Factors Related to Forest Fires in Different Forest Ecosystems in China

Forests are the largest terrestrial ecosystem with major benefits in three areas: economy, ecology, and society. However, the frequent occurrence of forest fires has seriously affected the structure and function of forests. To provide a strong scientific basis for forest fire prevention and control, Ripley’s K(d) function and the LightGBM algorithm were used to determine the spatial pattern of forest fires in four different provinces (Heilongjiang, Jilin, Liaoning, Hebei) in China from 2019 to 2021 and the impact of driving factors on different ecosystems. In addition, this study also identified fire hotspots in the four provinces based on kernel density estimation (KDE). An artificial neural network model (ANN) was created to predict the probability of occurrence of forest fires in the study area. The results showed that the forest fires were spatially clustered, but the variable importance of different factors varied widely among the different forest ecosystems. Forest fires in Heilongjiang and Liaoning Provinces were mainly caused by human-driven factors. For Jilin, meteorological factors were important in the occurrence of fires. Topographic and vegetation factors exhibited the greatest importance in Hebei Province. The selected driving factors were input to the ANN model to predict the probability of fire occurrence in the four provinces. The ANN model accurately captured 93.17%, 90.28%, 83.16%, and 89.18% of the historical forest fires in Heilongjiang, Jilin, Liaoning, and Hebei Provinces; Precision, Recall, and F-measure based on the full dataset are 0.87, 0.88, and 0.87, respectively. The results of this study indicated that there were differences in the driving factors of fire in different forest ecosystems. Different fire management policies must be formulated in response to this spatial heterogeneity

Biodegradable ZnLiCa ternary alloys for critical-sized bone defect regeneration at load-bearing sites: In vitro and in vivo studies

A novel biodegradable metal system, ZnLiCa ternary alloys, were systematically investigated both in vitro and in vivo. The ultimate tensile strength (UTS) of Zn0.8Li0.1Ca alloy reached 567.60 ± 9.56 MPa, which is comparable to pure Ti, one of the most common material used in orthopedics. The elongation of Zn0.8Li0.1Ca is 27.82 ± 18.35%, which is the highest among the ZnLiCa alloys. The in vitro degradation rate of Zn0.8Li0.1Ca alloy in simulated body fluid (SBF) showed significant acceleration than that of pure Zn. CCK-8 tests and hemocompatibility tests manifested that ZnLiCa alloys exhibit good biocompatibility. Real-time PCR showed that Zn0.8Li0.1Ca alloy successfully stimulated the expressions of osteogenesis-related genes (ALP, COL-1, OCN and Runx-2), especially the OCN. An in vivo implantation was conducted in the radius of New Zealand rabbits for 24 weeks, aiming to treat the bone defects. The Micro-CT and histological evaluations proved that the regeneration of bone defect was faster within the Zn0.8Li0.1Ca alloy scaffold than the pure Ti scaffold. Zn0.8Li0.1Ca alloy showed great potential to be applied in orthopedics, especially in the load-bearing sites

Structure Design of GFRP Composite Leaf Spring: An Experimental and Finite Element Analysis

Due to the high load-bearing capacity and light weight, composite leaf spring with variable width and variable thickness has been increasingly used in the automobile industry to replace the conventional steel leaf spring with a heavy weight. The optimum structural design of composite leaf spring is particularly favorable for the weight reduction. In this study, an effective algorithm is developed for structural optimization of composite leaf spring. The mechanical performance of composite leaf spring with designed dimensions is characterized using a combined experimental and computational approach. Specifically, the composite leaf spring with variable width and variable thickness was prepared using the filament winding process, and the three-dimensional finite element (FE) model of the designed composite leaf spring is developed. The experimental sample and FE model of composite leaf spring are tested under the three-point bending method. From experimental and simulation results, it is shown that the bending stiffness of the designed leaf spring meets the design requirement in the automotive industry, while the results of stress calculation along all directions meet the requirements of material strength requirement. The developed algorithm contributes to the design method for optimizing the stiffness and strength performance of the composite leaf spring

Biodegradable Zn–Sr alloy for bone regeneration in rat femoral condyle defect model: In vitro and in vivo studies

Bone defects are commonly caused by severe trauma, malignant tumors, or congenital diseases and remain among the toughest clinical problems faced by orthopedic surgeons, especially when of critical size. Biodegradable zinc-based metals have recently gained popularity for their desirable biocompatibility, suitable degradation rate, and favorable osteogenesis-promoting properties. The biphasic activity of Sr promotes osteogenesis and inhibits osteoclastogenesis, which imparts Zn–Sr alloys with the ideal theoretical osteogenic properties. Herein, a biodegradable Zn–Sr binary alloy system was fabricated. The cytocompatibility and osteogenesis of the Zn–Sr alloys were significantly better than those of pure Zn in MC3T3-E1 cells. RNA-sequencing illustrated that the Zn-0.8Sr alloy promoted osteogenesis by activating the wnt/β-catenin, PI3K/Akt, and MAPK/Erk signaling pathways. Furthermore, rat femoral condyle defects were repaired using Zn-0.8Sr alloy scaffolds, with pure Ti as a control. The scaffold-bone integration and bone ingrowth confirmed the favorable in vivo repair properties of the Zn–Sr alloy, which was verified to offer satisfactory biosafety based on the hematoxylin-eosin (H&E) staining and ion concentration testing of important organs. The Zn-0.8Sr alloy was identified as an ideal bone repair material candidate, especially for application in critical-sized defects on load-bearing sites due to its favorable biocompatibility and osteogenic properties in vitro and in vivo