Exoskeletons that assist the hip, knee, and ankle joints have begun to improve human mobility, particularly by reducing the metabolic cost of walking. However, direct comparisons of optimal assistance of these joints, or their combinations, have not yet been possible. Assisting multiple joints may be more beneficial than the sum of individual effects, because muscles often span multiple joints, or less effective, because single-joint assistance can indirectly aid other joints. In this study, we used a hip–knee–ankle exoskeleton emulator paired with human-in-the-loop optimization to find single-joint, two-joint, and whole-leg assistance that maximally reduced the metabolic cost of walking. Hip-only and ankle-only assistance reduced the metabolic cost of walking by 26 and 30% relative to walking in the device unassisted, confirming that both joints are good targets for assistance (N = 3). Knee-only assistance reduced the metabolic cost of walking by 13%, demonstrating that effective knee assistance is possible (N = 3). Two-joint assistance reduced the metabolic cost of walking by between 33 and 42%, with the largest improvements coming from hip-ankle assistance (N = 3). Assisting all three joints reduced the metabolic cost of walking by 50%, showing that at least half of the metabolic energy expended during walking can be saved through exoskeleton assistance (N = 4). Changes in kinematics and muscle activity indicate that single-joint assistance indirectly assisted muscles at other joints, such that the improvement from whole-leg assistance was smaller than the sum of its single-joint parts. Exoskeletons can assist the entire limb for maximum effect, but a single well-chosen joint can be more efficient when considering additional factors such as weight and cost.

Substantial progress has been made in the standardization of nomenclature for paediatric and congenital cardiac care. In 1936, Maude Abbott published her Atlas of Congenital Cardiac Disease, which was the first formal attempt to classify congenital heart disease. The International Paediatric and Congenital Cardiac Code (IPCCC) is now utilized worldwide and has most recently become the paediatric and congenital cardiac component of the Eleventh Revision of the International Classification of Diseases (ICD-11). The most recent publication of the IPCCC was in 2017. This manuscript provides an updated 2021 version of the IPCCC.

The International Society for Nomenclature of Paediatric and Congenital Heart Disease (ISNPCHD), in collaboration with the World Health Organization (WHO), developed the paediatric and congenital cardiac nomenclature that is now within the eleventh version of the International Classification of Diseases (ICD-11). This unification of IPCCC and ICD-11 is the IPCCC ICD-11 Nomenclature and is the first time that the clinical nomenclature for paediatric and congenital cardiac care and the administrative nomenclature for paediatric and congenital cardiac care are harmonized. The resultant congenital cardiac component of ICD-11 was increased from 29 congenital cardiac codes in ICD-9 and 73 congenital cardiac codes in ICD-10 to 318 codes submitted by ISNPCHD through 2018 for incorporation into ICD-11. After these 318 terms were incorporated into ICD-11 in 2018, the WHO ICD-11 team added an additional 49 terms, some of which are acceptable legacy terms from ICD-10, while others provide greater granularity than the ISNPCHD thought was originally acceptable. Thus, the total number of paediatric and congenital cardiac terms in ICD-11 is 367. In this manuscript, we describe and review the terminology, hierarchy, and definitions of the IPCCC ICD-11 Nomenclature. This article, therefore, presents a global system of nomenclature for paediatric and congenital cardiac care that unifies clinical and administrative nomenclature.

The members of ISNPCHD realize that the nomenclature published in this manuscript will continue to evolve. The version of the IPCCC that was published in 2017 has evolved and changed, and it is now replaced by this 2021 version. In the future, ISNPCHD will again publish updated versions of IPCCC, as IPCCC continues to evolve.

Microstructural analysis and bulk dielectric property analysis (real and imaginary permittivity at 95 GHz) were performed at temperatures ranging from 25 to 550 °C for ceramic composites comprising a hot-pressed aluminum nitride matrix (containing yttria and trace carbon as sintering additives) with molybdenum powder as a millimeter-wave radiation-absorbing additive. Loading percentages in the range of 0.25 vol% to 4.0 vol% Mo were characterized. For the temperature regime evaluated, the temperature-related changes in real and imaginary components of permittivity were found to be relatively modest compared with those driven by Mo loading. Energy-dispersive X-ray spectroscopic analysis of Mo grains and surrounding regions showed the presence of a mixed-phase layer, containing Mo2C, at the AlN–Mo interface. The Mo2C-containing mixed-phase layer, typically a few micrometers thick, surrounded the Mo grains. Further characterization of this mixed-phase layer is required to determine its contribution to the dielectric properties of the composite.

In order to begin to evaluate and model the suitability of high temperature ceramic composites, such as AlN:Mo, as susceptor materials for power beaming applications, the electromagnetic, thermal, and mechanical properties of the material must be known at elevated temperatures. Work reported here focuses on the development of thermal property datasets for AlN:Mo composites ranging from 0.25% to 4.0% Mo by volume. To calculate thermal conductivity of the AlN:Mo composite series, specific heat capacity, thermal diffusivity, and density data were acquired. The calculated specific heat capacity, Cp, of the set of AlN:Mo composites was, on average, found to be approximately 803 J/kgK at 100 °C and to increase to approximately 1133 J/kgK at 1000 °C, with all values to be within +/- 32 J/kgK of the average at a given temperature. These calculated specific heat capacity values matched values derived from DSC measurements to within the expected error of the measurements. Measured thermal diffusivity, α, of the set of AlN:Mo composites was, on average, found to be approximately 3.93 x 10-1 cm2/s at 100 °C and to increase to approximately 9.80 x 10-2 cm2/s at 1000 °C, with all values within +/- 1.84 x 10-2 cm2/s of the average at a given temperature. Thermal conductivity, k, for the set of AlN:Mo composites was found to be approximately 108 W/mK at 100 °C and to decrease to approximately 38 W/mK at 1000 °C, with all values within +/- 5.3 W/mK of the average at a given temperature. Data trends show that increasing Mo content correlates to lower values of of Cp, α, and k at a given temperature.

Political preferences in the United States are highly correlated with population density, at national, state, and metropolitan-area scales. Using new data from voter registration records, we assess the extent to which this pattern can be explained by geographic mobility. We find that the revealed preferences of voters who move from one residence to another correlate with partisan affiliation, though voters appear to be sorting on non-political neighborhood attributes that covary with partisan preferences rather than explicitly seeking politically congruent neighbors. But, critically, we demonstrate through a simulation study that the estimated partisan bias in moving choices is on the order of five times too small to sustain the current geographic polarization of preferences. We conclude that location must have some influence on political preference, rather than the other way around, and provide evidence in support of this theory.

Many studies on the adoption of precision technologies have generally used logit models to explain the adoption behavior of individuals. This study investigates factors affecting the intensity of precision agriculture technologies adopted by cotton farmers. Particular attention is given to the role of spatial yield variability on the number of precision farming technologies adopted, using a count data estimation procedure and farm-level data. Results indicate that farmers with more within-field yield variability adopted a higher number of precision agriculture technologies. Younger and better educated producers and the number of precision agriculture technologies used were significantly correlated. Finally, farmers using computers for management decisions also adopted a higher number of precision agriculture technologies.

Precision farming information demanded by cotton producers is provided by various suppliers, including consultants, farm input dealerships, University Extension systems, and media sources. Factors associated with the decisions to select among information sources to search for precision farming information are analyzed using a multivariate probit regression accounting for correlation among the different selection decisions. Factors influencing these decisions are age, education, and income. These findings should be valuable to precision farming information providers who may be able to better meet their target clientele needs.

This study identified the factors that influenced whether farmers in the Southeastern United States perceived an improvement in environmental quality from adopting precision farming technologies (PFTs). Farmers with larger farms or higher yields were more likely to believe that they observed positive externalities associated with PFTs. Farmers who found PFTs profitable or who believed input reduction was important had higher probabilities whereas those with higher incomes or who were more dependent on farm income were less likely to perceive such benefits. Interestingly, the importance of environmental quality and length of time using PFTs were not found to affect the probability of perceiving an improvement in environmental quality.

Producers' preferences for cottonseed with respect to price, seed type, yield, and fiber quality are examined by a willingness-to-pay approach via mail surveys. Results indicate a positive willingness to pay (WTP) for technology relative to conventional cottonseed, and WTP increases with the level of technology. Yield and quality also show a positive WTP. Larger farms have a higher WTP for technology, and farms with more farm labor have a lower WTP for technology. These results suggest economies of size in technology adoption (biotechnology is not size-neutral) and that labor and biotechnology are direct substitutes.

Probit analysis identified factors that influence the adoption of precision farming technologies by Southeastern cotton farmers. Younger, more educated farmers who operated larger farms and were optimistic about the future of precision farming were most likely to adopt site-specific information technology. The probability of adopting variable-rate input application technology was higher for younger farmers who operated larger farms, owned more of the land they farmed, were more informed about the costs and benefits of precision farming, and were optimistic about the future of precision farming. Computer use was not important, possibly because custom hiring shifts the burden of computer use to agribusiness firms.

Binary logit analysis was used to identify the factors influencing adoption of Global Positioning System (GPS) guidance systems by cotton farmers in 11 Mid-south and Southeastern states. Results indicate that adoption was more likely by those who had already adopted other precision-farming practices and had used computers for farm management. In addition, younger and more affluent farmers were more likely to adopt. Farmers with larger farms and with relatively high yields were also more likely to adopt. Education was not a significant factor in a farmer's decision to adopt GPS guidance systems.

Personal digital assistants (PDA) and handheld global positioning systems (GPS) have become increasingly important in cotton production but little is known about their use. This research analyzed the adoption of PDA/handheld GPS devices in cotton production. A younger farmer who used a computer in farm management and had a positive perception of Extension had a greater likelihood of adopting the devices. In addition, farmers who used complementary remote sensing, plant mapping, and grid soil sampling information were more likely to use PDA/handheld GPS devices. Finally, the COTMAN in-field decision support program from Extension also positively impacted adoption.