Academic literature on the topic 'Coefficient of weight'

Bones are multifunctional passive organs of movement that supports soft tissue and directly attached muscles. They also protect internal organs and are a reserve of calcium, phosphorus and magnesium. Each bone is covered with periosteum, and the adjacent bone surfaces are covered by articular cartilage. Histologically, the bone is an organ composed of many different tissues. The main component is bone tissue (cortical and spongy) composed of a set of bone cells and intercellular substance (mineral and organic), it also contains fat, hematopoietic (bone marrow) and cartilaginous tissue. Bones are a tissue that even in adult life retains the ability to change shape and structure depending on changes in their mechanical and hormonal environment, as well as self-renewal and repair capabilities. This process is called bone turnover. The basic processes of bone turnover are: • bone modeling (incessantly changes in bone shape during individual growth) following resorption and tissue formation at various locations (e.g. bone marrow formation) to increase mass and skeletal morphology. This process occurs in the bones of growing individuals and stops after reaching puberty • bone remodeling (processes involve in maintaining bone tissue by resorbing and replacing old bone tissue with new tissue in the same place, e.g. repairing micro fractures). It is a process involving the removal and internal remodeling of existing bone and is responsible for maintaining tissue mass and architecture of mature bones. Bone turnover is regulated by two types of transformation: • osteoclastogenesis, i.e. formation of cells responsible for bone resorption • osteoblastogenesis, i.e. formation of cells responsible for bone formation (bone matrix synthesis and mineralization) Bone maturity can be defined as the completion of basic structural development and mineralization leading to maximum mass and optimal mechanical strength. The highest rate of increase in pig bone mass is observed in the first twelve weeks after birth. This period of growth is considered crucial for optimizing the growth of the skeleton of pigs, because the degree of bone mineralization in later life stages (adulthood) depends largely on the amount of bone minerals accumulated in the early stages of their growth. The development of the technique allows to determine the condition of the skeletal system (or individual bones) in living animals by methods used in human medicine, or after their slaughter. For in vivo determination of bone properties, Abstract 10 double energy X-ray absorptiometry or computed tomography scanning techniques are used. Both methods allow the quantification of mineral content and bone mineral density. The most important property from a practical point of view is the bone’s bending strength, which is directly determined by the maximum bending force. The most important factors affecting bone strength are: • age (growth period), • gender and the associated hormonal balance, • genotype and modification of genes responsible for bone growth • chemical composition of the body (protein and fat content, and the proportion between these components), • physical activity and related bone load, • nutritional factors: – protein intake influencing synthesis of organic matrix of bone, – content of minerals in the feed (CA, P, Zn, Ca/P, Mg, Mn, Na, Cl, K, Cu ratio) influencing synthesis of the inorganic matrix of bone, – mineral/protein ratio in the diet (Ca/protein, P/protein, Zn/protein) – feed energy concentration, – energy source (content of saturated fatty acids - SFA, content of polyun saturated fatty acids - PUFA, in particular ALA, EPA, DPA, DHA), – feed additives, in particular: enzymes (e.g. phytase releasing of minerals bounded in phytin complexes), probiotics and prebiotics (e.g. inulin improving the function of the digestive tract by increasing absorption of nutrients), – vitamin content that regulate metabolism and biochemical changes occurring in bone tissue (e.g. vitamin D3, B6, C and K). This study was based on the results of research experiments from available literature, and studies on growing pigs carried out at the Kielanowski Institute of Animal Physiology and Nutrition, Polish Academy of Sciences. The tests were performed in total on 300 pigs of Duroc, Pietrain, Puławska breeds, line 990 and hybrids (Great White × Duroc, Great White × Landrace), PIC pigs, slaughtered at different body weight during the growth period from 15 to 130 kg. Bones for biomechanical tests were collected after slaughter from each pig. Their length, mass and volume were determined. Based on these measurements, the specific weight (density, g/cm3) was calculated. Then each bone was cut in the middle of the shaft and the outer and inner diameters were measured both horizontally and vertically. Based on these measurements, the following indicators were calculated: • cortical thickness, • cortical surface, • cortical index. Abstract 11 Bone strength was tested by a three-point bending test. The obtained data enabled the determination of: • bending force (the magnitude of the maximum force at which disintegration and disruption of bone structure occurs), • strength (the amount of maximum force needed to break/crack of bone), • stiffness (quotient of the force acting on the bone and the amount of displacement occurring under the influence of this force). Investigation of changes in physical and biomechanical features of bones during growth was performed on pigs of the synthetic 990 line growing from 15 to 130 kg body weight. The animals were slaughtered successively at a body weight of 15, 30, 40, 50, 70, 90, 110 and 130 kg. After slaughter, the following bones were separated from the right half-carcass: humerus, 3rd and 4th metatarsal bone, femur, tibia and fibula as well as 3rd and 4th metatarsal bone. The features of bones were determined using methods described in the methodology. Describing bone growth with the Gompertz equation, it was found that the earliest slowdown of bone growth curve was observed for metacarpal and metatarsal bones. This means that these bones matured the most quickly. The established data also indicate that the rib is the slowest maturing bone. The femur, humerus, tibia and fibula were between the values of these features for the metatarsal, metacarpal and rib bones. The rate of increase in bone mass and length differed significantly between the examined bones, but in all cases it was lower (coefficient b <1) than the growth rate of the whole body of the animal. The fastest growth rate was estimated for the rib mass (coefficient b = 0.93). Among the long bones, the humerus (coefficient b = 0.81) was characterized by the fastest rate of weight gain, however femur the smallest (coefficient b = 0.71). The lowest rate of bone mass increase was observed in the foot bones, with the metacarpal bones having a slightly higher value of coefficient b than the metatarsal bones (0.67 vs 0.62). The third bone had a lower growth rate than the fourth bone, regardless of whether they were metatarsal or metacarpal. The value of the bending force increased as the animals grew. Regardless of the growth point tested, the highest values were observed for the humerus, tibia and femur, smaller for the metatarsal and metacarpal bone, and the lowest for the fibula and rib. The rate of change in the value of this indicator increased at a similar rate as the body weight changes of the animals in the case of the fibula and the fourth metacarpal bone (b value = 0.98), and more slowly in the case of the metatarsal bone, the third metacarpal bone, and the tibia bone (values of the b ratio 0.81–0.85), and the slowest femur, humerus and rib (value of b = 0.60–0.66). Bone stiffness increased as animals grew. Regardless of the growth point tested, the highest values were observed for the humerus, tibia and femur, smaller for the metatarsal and metacarpal bone, and the lowest for the fibula and rib. Abstract 12 The rate of change in the value of this indicator changed at a faster rate than the increase in weight of pigs in the case of metacarpal and metatarsal bones (coefficient b = 1.01–1.22), slightly slower in the case of fibula (coefficient b = 0.92), definitely slower in the case of the tibia (b = 0.73), ribs (b = 0.66), femur (b = 0.59) and humerus (b = 0.50). Bone strength increased as animals grew. Regardless of the growth point tested, bone strength was as follows femur > tibia > humerus > 4 metacarpal> 3 metacarpal> 3 metatarsal > 4 metatarsal > rib> fibula. The rate of increase in strength of all examined bones was greater than the rate of weight gain of pigs (value of the coefficient b = 2.04–3.26). As the animals grew, the bone density increased. However, the growth rate of this indicator for the majority of bones was slower than the rate of weight gain (the value of the coefficient b ranged from 0.37 – humerus to 0.84 – fibula). The exception was the rib, whose density increased at a similar pace increasing the body weight of animals (value of the coefficient b = 0.97). The study on the influence of the breed and the feeding intensity on bone characteristics (physical and biomechanical) was performed on pigs of the breeds Duroc, Pietrain, and synthetic 990 during a growth period of 15 to 70 kg body weight. Animals were fed ad libitum or dosed system. After slaughter at a body weight of 70 kg, three bones were taken from the right half-carcass: femur, three metatarsal, and three metacarpal and subjected to the determinations described in the methodology. The weight of bones of animals fed aa libitum was significantly lower than in pigs fed restrictively All bones of Duroc breed were significantly heavier and longer than Pietrain and 990 pig bones. The average values of bending force for the examined bones took the following order: III metatarsal bone (63.5 kg) <III metacarpal bone (77.9 kg) <femur (271.5 kg). The feeding system and breed of pigs had no significant effect on the value of this indicator. The average values of the bones strength took the following order: III metatarsal bone (92.6 kg) <III metacarpal (107.2 kg) <femur (353.1 kg). Feeding intensity and breed of animals had no significant effect on the value of this feature of the bones tested. The average bone density took the following order: femur (1.23 g/cm3) <III metatarsal bone (1.26 g/cm3) <III metacarpal bone (1.34 g / cm3). The density of bones of animals fed aa libitum was higher (P<0.01) than in animals fed with a dosing system. The density of examined bones within the breeds took the following order: Pietrain race> line 990> Duroc race. The differences between the “extreme” breeds were: 7.2% (III metatarsal bone), 8.3% (III metacarpal bone), 8.4% (femur). Abstract 13 The average bone stiffness took the following order: III metatarsal bone (35.1 kg/mm) <III metacarpus (41.5 kg/mm) <femur (60.5 kg/mm). This indicator did not differ between the groups of pigs fed at different intensity, except for the metacarpal bone, which was more stiffer in pigs fed aa libitum (P<0.05). The femur of animals fed ad libitum showed a tendency (P<0.09) to be more stiffer and a force of 4.5 kg required for its displacement by 1 mm. Breed differences in stiffness were found for the femur (P <0.05) and III metacarpal bone (P <0.05). For femur, the highest value of this indicator was found in Pietrain pigs (64.5 kg/mm), lower in pigs of 990 line (61.6 kg/mm) and the lowest in Duroc pigs (55.3 kg/mm). In turn, the 3rd metacarpal bone of Duroc and Pietrain pigs had similar stiffness (39.0 and 40.0 kg/mm respectively) and was smaller than that of line 990 pigs (45.4 kg/mm). The thickness of the cortical bone layer took the following order: III metatarsal bone (2.25 mm) <III metacarpal bone (2.41 mm) <femur (5.12 mm). The feeding system did not affect this indicator. Breed differences (P <0.05) for this trait were found only for the femur bone: Duroc (5.42 mm)> line 990 (5.13 mm)> Pietrain (4.81 mm). The cross sectional area of the examined bones was arranged in the following order: III metatarsal bone (84 mm2) <III metacarpal bone (90 mm2) <femur (286 mm2). The feeding system had no effect on the value of this bone trait, with the exception of the femur, which in animals fed the dosing system was 4.7% higher (P<0.05) than in pigs fed ad libitum. Breed differences (P<0.01) in the coross sectional area were found only in femur and III metatarsal bone. The value of this indicator was the highest in Duroc pigs, lower in 990 animals and the lowest in Pietrain pigs. The cortical index of individual bones was in the following order: III metatarsal bone (31.86) <III metacarpal bone (33.86) <femur (44.75). However, its value did not significantly depend on the intensity of feeding or the breed of pigs.

Abstract This study explores how the damping coefficient affects vibration in small electric cars. By developing a mathematical model and simulating different damping coefficients, the authors found that this coefficient is crucial for controlling vehicle vibration, leading to a smoother and safer driving experience. Unlike traditional gasoline cars, small electric vehicles require different damping coefficients due to their distinct weight and structure. The study offers specific recommendations for optimizing damping coefficients in current electric car models, aiming to enhance user comfort and safety. This research is vital for improving small electric vehicles in Vietnam and supporting the shift to green transportation. Detailed findings are available in the full research paper.

AbstractPath tracking is a crucial function for achieving unmanned driving. This paper addresses the challenge of low tracking accuracy and poor stability in driverless trucks caused by uncertain model parameters and steady-state errors during path tracking. A linear quadratic regulator (LQR) controller optimization by an improved genetic algorithm has been designed. Firstly, the paper formulates the dynamic model of a two-degree-of-freedom vehicle as well as the model for tracking error. Subsequently, path tracking control is achieved through the utilization of feedforward control and LQR feedback control algorithms. Secondly, the weight coefficient of the LQR controller is enhanced through the utilization of an improved GA in order to boost the precision of path tracking. Ultimately, the devised controller undergoes simulation and validation in the TruckSim-Matlab/Simulink platform across diverse operational circumstances. The findings from the simulation demonstrate that the controller, optimized through improvements in the genetic algorithm, exhibits excellent tracking accuracy and stability.

Aircraft Rotor Smoothing (aka rotor track and balance) is a complicated process that can be very challenging for even the most experienced maintainers. Rotor smoothing is required to reduce a helicopter's 1/revolution vibration levels. This improves crew comfort and reduces crew fatigue. Maintainers make mechanical adjustments to a rotor system that experiences various dynamic and aerodynamic forces. The typical rotor smoothing event involves balance weight adjustments, pitch link adjustments for changes in blade angle of attack, and blade trailing edge tab adjustments that affect a blade's pitching moment and thus its lift. The rotor smoothing equipment used to determine these adjustments incorporate imbedded empirically developed algorithms and coefficients. It is imperative that these algorithms and coefficients are properly developed. Open lines of communication with aircraft manufacturers are essential due to the differences in aircraft and rotor systems design. The differences in aircraft design may require rotor smoothing methods that are unique to its airframe in order to guarantee successful rotor smoothing events.

A mathematical description for an optimum balance weight search algorithm for single plane multipoint balance is presented. The algorithm uses influence coefficients, either measured or known beforehand, and measured complex vibration data to determine an optimum balance correction weight. The solution minimizes the maximum residual vibration. The algorithm allows user defined balance weights to be analyzed and evaluated. A test case is presented showing actual results and comparison with a least square solution algorithm. An efficient multiplane influence coefficient calculation scheme is also presented.

There is current interest in the use of hydrogen to meet the climate change decarbonization goals for energy and transportation. While hydrogen is currently produced and transported in large quantities in the United States, the majority is transported through dedicated hydrogen pipeline systems. Very little is known about the operational risks associated with injecting hydrogen into pipelines that have been optimized for natural gas or repurposing those pipelines for pure hydrogen transportation. Hydrogen has widely different thermodynamic properties compared to methane dominant natural gas, most notably a negative Joule-Thomson coefficient and a compressibility factor that exceeds 1.0 for low pressures. Advantages to mixing hydrogen into natural gas are the lower pressure drop across long distances if volumetric flow is maintained, and the potential infrastructure savings from leveraging existing natural gas pipelines. Disadvantages include a lower overall energy content. Hydrogen has 1/3 the volumetric energy density of methane due to its low molecular weight (3 vs 10 kWh/Nm3), meaning storage and transport energy capacity is reduced unless the volume of gas transported is significantly increased. Report developed under the Emerging Fuels Institute with PRCI. Document security and set such that Only EFI members can download report.

The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. The Claim (Section 2) describes the biomarker performance and is derived from the body of scientific literature meeting specific requirements, in particular test-retest studies. The Activities (Section 3) contribute to generating the biomarker. Requirements are placed on the Actors that participate in those activities as necessary to achieve the Claim. Assessment Procedures (Section 4) for evaluating specific requirements are defined as needed to ensure acceptable performance. Diffusion-Weighted Imaging (DWI) and the Apparent Diffusion Coefficient (ADC) are being used clinically as qualitative (DWI) and quantitative (ADC) indicators of disease presence, progression or response to treatment. Use of ADC as a robust quantitative biomarker with finite confidence intervals places additional requirements on Sites, Acquisition Devices and Protocols, Field Engineers, Scanner Operators (MR Technologists, Radiologists, Physicists and other Scientists), Image Analysts, Reconstruction Software and Image Analysis Tools. Additionally, due to the intrinsic dependence of measured ADC values on biophysical tissue properties, both the Profile Claims and the associated scan protocols (Section 3.6.2) are organ-specific. All of these are considered Actors involved in Activities of Acquisition Device Pre-delivery and Installation, Subject Handling, Image Data Acquisition, Reconstruction, Registration, ADC map generation, Quality Assurance (QA), Distribution, Analysis, and Interpretation. The requirements addressed in this Profile are focused on achieving ADC values with minimal systematic bias and measurement variability. DISCLAIMER: Technical performance of the MRI system can be assessed using a phantom having known diffusion properties, such as the QIBA DWI phantom. The clinical performance target is to achieve a 95% confidence interval for measurement of ADC with a variable precision depending on the organ being imaged and assuming adequate technical performance requirements are met. While in vivo DWI/ADC measurements have been performed throughout the human body, this Profile focused on four organ systems, namely brain, liver, prostate, and breast as having high clinical utilization of ADC with a sufficient level of statistical evidence to support the Profile Claims derived from the current peer-reviewed literature. In due time, new DWI technologies with proven greater performance levels, as well as more organ systems will be incorporated in future Profiles. This document is intended to help a variety of users: clinicians using this biomarker to aid patient management; imaging staff generating this biomarker; MRI system architects developing related products; purchasers of such products; and investigators designing clinical trials utilizing quantitative diffusion-based imaging endpoints. Note that this document only states requirements specific to DWI to achieve the claim, not requirements that pertain to clinical standard of care. Conforming to this Profile is secondary to proper patient care.

Heat production (HP) or energy expenditure for activity (EEa) is of fundamental nutritional importance for livestock because it determines the proportion of ingested nutrients available for productive functions. Previous estimates of EEa are unreliable and vary widely with different indirect methodologies. This leads to erroneous nutritional strategies, especially when intake on pasture does not meet nutritional requirements and supplementation is necessary for acceptable production. Therefore, the objective of this project was to measure EEa in different classes of livestock (beef cattle and goats) over a wide range of ecological and management conditions to develop and evaluate simple means of prediction. In the first study in Israel, small frame (SF) and large frame (LF) cows (268 and 581 kg) were monitored during spring, summer, and autumn. Feed intake by SF cows per unit of metabolic weight was greater (P < 0.001) than that by LF cows in both spring and summer and their apparent selection of higher quality herbage in spring was greater (P < 0.10) than that of LF cows. SF cows grazed more hours per day and walked longer distances than the LF cows during all seasons. The coefficient of specific costs of activities (kJ•kg BW-0.75•d-1) and of locomotion (J•kg BW-0.75•m-1) were smaller for the SF cows. In the second study, cows were monitored in March, May, and September when they grazed relatively large plots, 135 and 78 ha. Energy cost coefficients of standing, grazing, and horizontal locomotion derived were similar to those of the previous study based on data from smaller plots. However, the energy costs of walking idle and of vertical locomotion were greater than those found by Brosh et al. (2006) but similar to those found by Aharoni et al. (2009). In the third study, cows were monitored in February and May in a 78-ha plot with an average slope of 15.5°, whereas average plot slopes of the former studies ranged between 4.3 and 6.9°. Energy cost coefficients of standing, grazing, and walking idle were greater than those calculated in the previous studies. However, the estimated energy costs of locomotion were lower in the steeper plot. A comparison on a similar HP basis, i.e., similar metabolizable energy (ME) intake, shows that the daily energy spent on activities in relation to daily HP increased by 27% as the average plot slope increased from 5.8 and 6.02 to 15.5°. In the fourth study, cows grazing in a woodland habitat were monitored as in previous studies in December, March, and July. Data analysis is in progress. In the first US experiment, Boer and Spanish does with two kids were used in an experiment beginning in late spring at an average of 24 days after kidding. Two does of each breed resided in eight 0.5-ha grass/forb pastures. Periods of 56, 60, 63, 64, and 73 days in length corresponded to mid-lactation, early post-weaning, the late dry period, early gestation, and mid-gestation. EEa expressed as a percentage of the ME requirement for maintenance plus activity in confinement (EEa%) was not influenced by stocking rate, breed, or period, averaging 49%. Behavioral activities (e.g., time spent grazing, walking, and idle, distance traveled) were not highly related to EEa%, although no-intercept regressions against time spent grazing/eating and grazing/eating plus walking indicated an increase in EEa% of 5.8 and 5.1%/h, respectively. In the second study, animal types were yearling Angora doeling goats, yearling Boer wether goats, yearling Spanish wether goats, and Rambouilletwether sheep slightly more than 2 yr of age. Two animals of each type were randomly allocated to one of four pastures 9.3, 12.3, 4.6, and 1.2 ha in area. The experiment was conducted in the summer with three periods, 30, 26, and 26 days in length. EEa% was affected by an interaction between animal type and period (Angora: 16, 17, and 15; Boer: 60, 67, and 34; Spanish: 46, 62, and 42; sheep: 22, 12, and 22% in periods 1, 2, and 3, respectively (SE = 6.1)). EEa% of goats was predicted with moderate accuracy (R2 = 0.40-0.41) and without bias from estimates of 5.8 and 5.1%/h spent grazing/eating and grazing/eating plus walking, respectively, determined in the first experiment; however, these methods were not suitable for sheep. These methods of prediction are simpler and more accurate than currently recommended for goats by the National Research Council.

This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions. The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The skew coefficient values for each streamgage were then computed as the variance-weighted average of at-site and regional skew coefficients. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter. This report also provides: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged sites and to improve flood-quantile estimates at and near a gaged site; (2) the urbanization-adjusted annual maximum peak discharges and peak discharge quantile estimates at streamgages from 181 watersheds including the 117 study watersheds and 64 additional watersheds in the study region that were originally considered for use in the study but later deemed to be redundant. The urbanization-adjustment equations, spatial regression equations, and peak discharge quantile estimates developed in this study will be made available in the web-based application StreamStats, which provides automated regression-equation solutions for user-selected stream locations. Figures and tables comparing the observed and urbanization-adjusted peak discharge records by streamgage are provided at http://dx.doi.org/10.3133/sir20165050 for download.

Network Analysis of a Mobility Ecosystem in Detroit, MI Background As part of a new initiative from the Global Epicenter of Mobility (GEM), organizations across many sectors in Detroit, MI, and surrounding counties are collaboratively investing in transforming the local legacy mobility industry into an inclusive advanced mobility cluster over the next 3 years. At the start of this initiative, in partnership with the research team at the Detroit Regional Partnership, a social network analysis was conducted to map the relationship between the foundational 24 organizations, the greater coalition, and their extended network to date. The organizations within this initiative were divided into 4 sectors that highlighted key differences in engagement This baseline map and relationship data, as well as key network analysis metrics, will be compared to future data collections over the coming years to track the initiative’s progress. Methods The original coalition (161 organizations) was identified by the local partner organization and data collection proceeded from September-December 2023 through survey completion. One or more representatives of coalition organizations were asked to identify their relationship to other members of the coalition using a 1-5 scale (Frey et al., 2006). Data were analyzed in R, and organization-level metrics, as well as centralized network-wide metrics, were produced for weighted betweenness, degree, and weighted degree centrality, as well as averages of connection strength. Maps were produced using KUMU software. Findings The mobility coalition consisted of 159 nodes and 7412 connections. Of those connections, 3763 (50.77%) had at least a level 1 connection strength, while 2319 (31.29%) had a connection strength of ≧ 3 (an active working relationship). The average connection strength for the network was 2.13. The coalition network was highly interconnected, with a clustering coefficient of 0.70 and a density of 0.59. Nonprofit and foundation organizations made up 47.5-50% of the top quartile by all centrality metrics while only making up 32.1% of the network. Corporate and private organizations made up 42.8% of the network and made up 68.42-82.50% of the bottom quartile across all metrics. The distribution of centrality scores of the corporate and private organizations was significantly lower than those of all other sectors within the network. These results illuminate a network that is highly interconnected, but in which not all sectors are engaging equally. These results are being used to plan and implement strategic interventions to foster new relationships and growth within the network. In addition to the 159 coalition organizations, respondents to the survey identified an additional 244 organizations as active participants within the Detroit region mobility space. A select number of these organizations will be added to the coalition as it becomes established within the Detroit region. These provide directions for future growth of the GEM initiative and the mobility ecosystem network and are examples of turning research into action.

Network Analysis of a Mobility Ecosystem in Detroit, MI Background As part of a new initiative from the Global Epicenter of Mobility (GEM), organizations across many sectors in Detroit, MI, and surrounding counties are collaboratively investing in transforming the local legacy mobility industry into an inclusive advanced mobility cluster over the next 3 years. At the start of this initiative, in partnership with the research team at the Detroit Regional Partnership, a social network analysis was conducted to map the relationship between the foundational 24 organizations, the greater coalition, and their extended network to date. The organizations within this initiative were divided into 4 sectors that highlighted key differences in engagement This baseline map and relationship data, as well as key network analysis metrics, will be compared to future data collections over the coming years to track the initiative’s progress. Methods The original coalition (161 organizations) was identified by the local partner organization and data collection proceeded from September-December 2023 through survey completion. One or more representatives of coalition organizations were asked to identify their relationship to other members of the coalition using a 1-5 scale (Frey et al., 2006). Data were analyzed in R, and organization-level metrics, as well as centralized network-wide metrics, were produced for weighted betweenness, degree, and weighted degree centrality, as well as averages of connection strength. Maps were produced using KUMU software. Findings The mobility coalition consisted of 159 nodes and 7412 connections. Of those connections, 3763 (50.77%) had at least a level 1 connection strength, while 2319 (31.29%) had a connection strength of ≧ 3 (an active working relationship). The average connection strength for the network was 2.13. The coalition network was highly interconnected, with a clustering coefficient of 0.70 and a density of 0.59. Nonprofit and foundation organizations made up 47.5-50% of the top quartile by all centrality metrics while only making up 32.1% of the network. Corporate and private organizations made up 42.8% of the network and made up 68.42-82.50% of the bottom quartile across all metrics. The distribution of centrality scores of the corporate and private organizations was significantly lower than those of all other sectors within the network. These results illuminate a network that is highly interconnected, but in which not all sectors are engaging equally. These results are being used to plan and implement strategic interventions to foster new relationships and growth within the network. In addition to the 159 coalition organizations, respondents to the survey identified an additional 244 organizations as active participants within the Detroit region mobility space. A select number of these organizations will be added to the coalition as it becomes established within the Detroit region. These provide directions for future growth of the GEM initiative and the mobility ecosystem network and are examples of turning research into action.

Fabricated joints are gradually applied in architectural structures because of their advantages of good economy, high installation quality and efficiency. However, the mechanical properties of this kind of joint are semi-rigid differing from traditional rigid and hinged joints. Therefore, the performance of the structures with such joints is not clear, which greatly limits the wide application of fabricated joints. This paper presents the investigation on the seismic performance of the semi-rigid single-layer reticulated shell structure (SRSS) under earthquake load by numerical simulation and theoretical analysis. A finite element model (FEM) of the semi-rigid reticulated shell was established. The influence of joint stiffness on the seismic performance of semi-rigid SRSS was obtained by taking both initial defects and material damage accumulation into account. The two design parameters, limit stiffness ratio and limit yield moment of the joints, were proposed for the semi-rigid reticulated shells. The influence of the roof span, roof weight and member section on the two design parameters was obtained and the calculation formula was established. The seismic force coefficient for the semi-rigid SRSS was obtained, which can provide support for the seismic design of semi-rigid SRSS.