The Variability and Evaluation Method of Recycled Concrete Aggregate Properties

With the same sources and regeneration techniques, given RA’s properties may display large variations. The same single property index of different sets maybe has a large difference of the whole property. How shall we accurately evaluate the whole property of RA? 8 groups of RAs from pavement and building were used to research the method of evaluating the holistic characteristics of RA. After testing and investigating, the parameters of aggregates were analyzed. The data of physical and mechanical properties show a distinct dispersion and instability; thus, it has been difficult to express the whole characteristics in any single property parameter. The Euclidean distance can express the similarity of samples. The closer the distance, the more similar the property. The standard variance of the whole property Euclidean distances for two types of RA is Sk=7.341 and Sk=2.208, respectively, which shows that the property of building RA has great fluctuation, while pavement RA is more stable. There are certain correlations among the apparent density, water absorption, and crushed value of RAs, and the Mahalanobis distance method can directly evaluate the whole property by using its parameters: mean, variance, and covariance, and it can provide a grade evaluation model for RAs

Evaluation of production of Cheddar cheese from micellar casein concentrate

peer-reviewedThe production of Cheddar cheese using micellar casein concentrate (MCC), a novel milk ingredient powder with a high casein content (∼92%), was evaluated. Four types of Cheddar cheese were manufactured and ripened for 180 days from the following starting materials: standardised control milk (control), skim milk with cream (SC), reconstituted MCC with cream (MC) and reconstituted low-heat skim milk powder with cream (PC). Only minor differences were found in composition between treatments, but MC cheese showed higher levels of proteolysis compared with other treatments, linked to significantly higher plasmin and chymosin activities. No differences were observed in hardness between treatments (60, 120 and 180 days), but the springiness and cohesiveness of MC and PC cheeses were significantly higher than that of the control and SC cheeses at 60, 120 and 180 days. The use of casein-dominant dairy streams thus has the potential for production of Cheddar cheese with tailored functionality.Department of Agriculture, Food and the Marine, Irelan

Understanding preferences for and consumer behavior toward cheese among a cohort of young, educated, internationally mobile Chinese consumers

peer-reviewedThis study explores the experiences of a cohort of young, educated, internationally mobile Chinese consumers with cheese and other dairy products, and how these experiences shape their behavior toward cheese products. In total, 41 Chinese students studying at an Irish university participated in 5 focus groups (n = 41, n = 7–10). Thematic analysis identified important factors that influence consumer behaviors regarding cheese products. Individuals' expectations toward cheese were embedded in their knowledge structures, which were constructed from previous experience. Participants had general positive expectations toward cheese due to associations with western-style foods and nostalgia; however, direct eating experience determined long-term behavior. When making a purchase decision, choice motives were weighed and negotiated to establish a fundamental driving factor for purchase. Perceived probability of choice motive fulfillment was important in determining purchase decisions, with many participants having low perceived ability to select cheese and limited motivation to engage with cheese due to limited perceived relevance of cheese to their daily food life. Individuals' innovativeness was an important factor that influences their openness to cheese products when moving beyond familiar foods. Opportunities exist such as using nostalgic cues as marketing tools to increase consumers' interest in cheese or combining cheese with Chinese food to increase perceived relevance of cheese to their daily food life. Providing information at point of purchase could reduce the disconnect between expectation and actual experience, and innovative cheese products may be developed to better fulfill important choice motives

Deciphering the influence of TOD on metro ridership: An integrated approach of extended node-place model and interpretable machine learning with planning implications

Many global high-density cities have embraced transit-oriented development (TOD) strategies around metro stations in a strong push toward promoting transit trips. However, the general TOD principles must be adapted to a variety of local features. Thus, conducting a baseline study that deciphers the locality-specific differences and similarities in the influence of TOD on metro ridership is of paramount importance. Using the case of Shanghai in China, this paper demonstrated a novel approach to unveiling the locality-specific influence of TOD on metro ridership that integrated the node-functionality-place model with interpretable machine learning. By exploring a wide range of explanatory variables, we discovered that the relative importance of TOD structural factors (e.g., node, place and functionality) and neighborhood sociodemographics as well as their interactions presented great spatial and temporal heterogeneity for boarding and alighting on weekdays and weekends. Among the TOD structural factors, functionality was of the highest importance, and a greater contribution was observed within the central districts during peak hours. Regarding the interactions among the TOD structural factors, the interaction between node and functionality presented the highest relative importance, followed by that between place between functionality and that between node and place. Additionally, neighborhood sociodemographics also accounted for a noticeable contribution, especially in the outskirts. Based on the locality-specific estimations, the affinity propagation clustering algorithm was further used to cluster the TODs into difference groups as nuanced representations of the TOD-metro ridership relationships. The findings refreshed the knowledge base concerning the spatiotemporal heterogeneity in the nonlinear influence of TOD on metro ridership and provided new insights into spatial planning. The proposed approach showed high computational feasibility with strong theoretical underpinnings and thus offered broad potential applications into other high-density cities worldwide

Terrain Analytics for Precision Agriculture with Automated Vehicle Sensors and Data Fusion

Precision agriculture aims to use minimal inputs to generate maximal yields by managing the plant and its environment at a discrete instead of a field level. This new farming methodology requires localized field data including topological terrain attributes, which influence irrigation, field moisture, nutrient runoff, soil compaction, and traction and stability for traversing agriculture machines. Existing research studies have used different sensors, such as distance sensors and cameras, to collect topological information, which may be constrained by energy cost, performance, price, etc. This study proposed a low-cost method to perform farmland topological analytics using sensor implementation and data processing. Inertial measurement unit sensors, which are widely used in automated vehicle study, and a camera are set up on a robot vehicle. Then experiments are conducted under indoor simulated environments that include five common topographies that would be encountered on farms, combined with validation experiments in a real-world field. A data fusion approach was developed and implemented to track robot vehicle movements, monitor the surrounding environment, and finally recognize the topography type in real time. The resulting method was able to clearly recognize topography changes. This low-cost and easy-mount method will be able to augment and calibrate existing mapping algorithms with multidimensional information. Practically, it can also achieve immediate improvement for the operation and path planning of large agricultural machines

Modulating the tumor microenvironment with new therapeutic nanoparticles: a promising paradigm for tumor treatment

To better make nanomedicine entering the clinic, developing new rationally designed nanotherapeutics with a deeper understanding of tumor biology is required. The tumor microenvironment is similar to the inflammatory response in a healing wound, the milieu of which promotes tumor cell invasion and metastasis. Successful targeting of the microenvironmental components with effective nanotherapeutics to modulate the tumor microvessels or restore the homeostatic mechanisms in the tumor stroma will offer new hope for cancer treatment. We here highlight the progress in constructing nanotherapeutics to target or modulate the tumor microenvironment. We discuss the factors necessary for nanomedicines to become a new paradigm in cancer therapy, including the selection of drugs and therapeutic targets, controllable synthesis, and tempo-spatial drug release

Research on a Conflict Early Warning System Based on the Active Safety Concept

In order to reduce traffic conflicts on cross-intensive roads, this paper proposes a new early warning system based on the active safety concept. The system collects real-time vehicle data using roadside sensors and transmits the results to drivers on the major road in a timely manner via roadside warning lights. In this research, the principles of the warning system are discussed in detail, including how the vehicle dynamics data are collected and how potential collisions are identified and avoided. Through a driving simulation experiment, the speed prediction model after implementation of the warning system was examined. Results indicated that it can accurately identify the vehicle operating status, accurately guide driving behavior, and effectively reduce traffic conflict. To verify the reliability of the proposed warning logic and algorithm, numerical simulations were carried out via CarSim/Simulink cosimulation. The simulation results indicate that the proposed system enables drivers to perceive conflicting vehicles in advance, avoid the sudden braking phenomenon, and ensure safe driving

A Population Spatialization Model at the Building Scale Using Random Forest

Population spatialization reveals the distribution and quantity of the population in geographic space with gridded population maps. Fine-scale population spatialization is essential for urbanization and disaster prevention. Previous approaches have used remotely sensed imagery to disaggregate census data, but this approach has limitations. For example, large-scale population censuses cannot be conducted in underdeveloped countries or regions, and remote sensing data lack semantic information indicating the different human activities occurring in a precise geographic location. Geospatial big data and machine learning provide new fine-scale population distribution mapping methods. In this paper, 30 features are extracted using easily accessible multisource geographic data. Then, a building-scale population estimation model is trained by a random forest (RF) regression algorithm. The results show that 91% of the buildings in Lin’an District have absolute error values of less than six compared with the actual population data. In a comparison with a multiple linear (ML) regression model, the mean absolute errors of the RF and ML models are 2.52 and 3.21, respectively, the root mean squared errors are 8.2 and 9.8, and the R2 values are 0.44 and 0.18. The RF model performs better at building-scale population estimation using easily accessible multisource geographic data. Future work will improve the model accuracy in densely populated areas

A Population Spatialization Model at the Building Scale Using Random Forest

Population spatialization reveals the distribution and quantity of the population in geographic space with gridded population maps. Fine-scale population spatialization is essential for urbanization and disaster prevention. Previous approaches have used remotely sensed imagery to disaggregate census data, but this approach has limitations. For example, large-scale population censuses cannot be conducted in underdeveloped countries or regions, and remote sensing data lack semantic information indicating the different human activities occurring in a precise geographic location. Geospatial big data and machine learning provide new fine-scale population distribution mapping methods. In this paper, 30 features are extracted using easily accessible multisource geographic data. Then, a building-scale population estimation model is trained by a random forest (RF) regression algorithm. The results show that 91% of the buildings in Lin’an District have absolute error values of less than six compared with the actual population data. In a comparison with a multiple linear (ML) regression model, the mean absolute errors of the RF and ML models are 2.52 and 3.21, respectively, the root mean squared errors are 8.2 and 9.8, and the R2 values are 0.44 and 0.18. The RF model performs better at building-scale population estimation using easily accessible multisource geographic data. Future work will improve the model accuracy in densely populated areas

Unraveling the relative contribution of TOD structural factors to metro ridership: A novel localized modeling approach with implications on spatial planning

TOD (transit-oriented development) has gradually earned the reputation as a promising spatial planning strategy to encourage public transit usage. Many cities across the world, especially those in the global south, have established TOD projects around metro stations. From this standpoint, the paramount question of how TOD is conductive to metro ridership is at the heart of scholarly discourse. Theoretically, the territorial organization of a TOD comprises three structural factors –node (metro station), place (surrounding land use) and their feedback. An accurate judgement of the TOD-metro ridership relationship would not be achieved if we neglected any of the three structural factors forming the TOD architecture. However, the three TOD structural factors have not frequently been considered simultaneously in prior literature. A gap remains regarding the relative contribution of TOD structural factors to metro ridership across time and space. This paper aims to address this unresolved issue using a case study of the Hangzhou metropolitan area in China. As the dependent variable, metro ridership is measured using one week of smart card records, with the three TOD structural factors as explanatory variables and sociodemographic factors as control variables, described by a set of indicators. A novel localized modeling approach using variance decomposition of geographically temporally weighted regression is demonstrated to quantify the spatially and temporally varying relative contribution of TOD structural factors. The results show that both similarities and discrepancies were identified compared to earlier studies. Most importantly, new knowledge was gained, particularly that ‘metro station’ factors contribute the most to metro ridership, followed by the feedback factor, ‘surrounding land use’ factors and sociodemographic factors both on workdays and nonworkdays. Furthermore, our analysis highlights that the relative contribution of TOD structural factors presents noticeable spatial heterogeneities across metro station areas both on workdays and nonworkdays but exhibits only obvious temporal heterogeneity on nonworkdays. Finally, TOD clusters are generated based on the relative contribution of TOD structural factors with implications for spatial planning. This study is believed to open the door for framing locally representative strategies of TOD to stimulate the use of public transit