Human Appropriation of Net Primary Production: From a Planet to a Pixel

Human appropriation of net primary production (HANPP) is a substantial improvement upon 20th century attempts at developing an ecological footprint indicator because of its measurability in relation to net primary production, its close relationship to other key footprint measures, such as carbon and water, and its spatial specificity. This paper explores HANPP across four geographical scales: through literature review, the planet; through reanalysis of existing data, variations among the world’s countries; and through novel analyses, U.S. counties and the 30 m pixel scale for one U.S. county. Results show that HANPP informs different sustainability narratives at different scales. At the planetary scale, HANPP is a critical planetary limit that improves upon areal land use indicators. At the country macroscale, HANPP indicates the degree to which meeting the needs of the domestic population for provisioning ecosystem services (food, feed, biofiber, biofuel) presses against the domestic ecological endowment of net primary production. At the county mesoscale, HANPP reveals the dependency of metropolitan areas upon regional specialized rural forestry and agroecosystems to which they are teleconnected through trade and transport infrastructures. At the pixel microscale, HANPP provides the basis for deriving spatial patterns of remaining net primary production upon which biodiversity and regulatory and cultural ecosystem services are dependent. HANPP is thus a sustainability indicator that can fulfill similar needs as carbon, water and other footprints

Evaluation of productive indicators in crossbred does with a diet based on forage and homemade concentrate

Resumen Con el objetivo de evaluar el comportamiento productivo de conejas mestizas se utilizaron un total de 24 reproductoras, entre 10 y 18 meses de edad, con un peso promedio superior a los 3,0 kg durante cuatro meses. Las reproductoras consumieron una dieta constituida por: forraje de morera (Morus alba): 0,30 kg; caña (Saccharum officinarum) molida: 0,25 kg, glycine (Neonotonia wightii): 0,40 kg y pienso criollo: 0,06 kg. Las crías se pesaron al nacimiento, a los 20 y a los 45 días de edad. En las reproductoras se controló las crías nacidas vivas y destetadas por parto. Se obtuvo como promedio 6,4 gazapos vivos por parto con 0,054 kg de PV al nacimiento y se destetaron 5,4 crías a los 45 días de edad con un peso de 0,694 kg. Además, se encontró un 84,4% de supervivencia durante la etapa de lactación. La ganancia media diaria durante la lactancia fue de 0,014 kg/animal/día. Los resultados evidenciaron que con la dieta propuesta se obtuvieron indicadores productivos alentadores en reproductoras mestizas. Palabras clave: Comportamiento, conejo, forraje verde, piensos Abstract With the objective of evaluating the productive performance of crossbred does, a total of 24 animals were used, between 10 and 18 months old, with average weight higher than 3,0 kg, during four months. The does ate a diet constituted by mulberry (Morus alba) forage: 0,30 kg; ground sugarcane (Saccharum officinarum): 0,25 kg; glycine (Neonotonia wightii): 0,40 kg and homemade concentrate: 0,06 kg. The young rabbits were weighed at birth, 20 and 45 days after birth. In the does the offspring born alive and weaned per parturition, were controlled. As average, 6,4 live rabbits were obtained per parturition, with 0,054 kg LW at birth and 5,4 rabbits were weaned being 45 days old and weighing 0,694 kg. In addition, 84,4% survival was found during the lactation stage. The mean daily gain during lactation was 0,014 kg/animal/day. The results proved that with the proposed diet encouraging productive indicators were obtained in crossbred does

Massive Wireless Energy Transfer with Multiple Power Beacons for very large Internet of Things

The Internet of Things (IoT) comprises an increasing number of low-power and low-cost devices that autonomously interact with the surrounding environment. As a consequence of their popularity, future IoT deployments will be massive, which demands energy-efficient systems to extend their lifetime and improve the user experience. Radio frequency wireless energy transfer has the potential of powering massive IoT networks, thus eliminating the need for frequent battery replacement by using the so-called power beacons (PBs). In this paper, we provide a framework for minimizing the sum transmit power of the PBs using devices' positions information and their current battery state. Our strategy aims to reduce the PBs' power consumption and to mitigate the possible impact of the electromagnetic radiation on human health. We also present analytical insights for the case of very distant clusters and evaluate their applicability. Numerical results show that our proposed framework reduces the outage probability as the number of PBs and/or the energy demands increase.Comment: 7 pages, 6 figures, Submitted to "The International Workshop on Very Large Internet of Things (2021)

Book Review Rural Education in America: What Works for Our Students, Teachers, and Communities

Access the online Pressbooks version of this article here. Book review of Marietta, G. & S. Marietta. (2020). Rural Education in America, What works for our students, teachers, and communities, Harvard Education Press. Statewide faculty teaching in rural Utah review this book and focus on actions to meet the specific needs of their demographic of rural students in rural communities. The reviewer’s reflections on the book developed from a Spring 2022 Empowering Teaching Excellence Learning Circle led by the primary author

Validation of a specific measure to assess health-related quality of life in patients with schizophrenia and bipolar disorder: the 'Tolerability and quality of life' (TOOL) questionnaire

<p>Abstract</p> <p>Background</p> <p>Perception of quality of life may differ depending on the perspective. The aim of the study was to assess the psychometric properties of the Spanish version of the 'TOlerability and quality Of Life' (TOOL) questionnaire, a specific self-rated instrument to evaluate the impact of side effects of antipsychotic drugs on health-related quality of life (HRQoL). The questionnaire consists of eight items answered on a four-point Likert scale.</p> <p>Methods</p> <p>A psychometric study was conducted with clinically stable outpatients with schizophrenia and bipolar disorder under antipsychotic treatment. The translation and cultural adaptation of the questionnaire was performed according to international standards. Internal consistency using the Cronbach α coefficient and test-retest reliability using the intraclass correlation coefficient (ICC) was used to assess the reliability of the instrument. Patients completed generic and specific measures of quality of life and clinical severity.</p> <p>Results</p> <p>A total of 238 patients were analysed, with a mean age of 42 years (SD 10.9). The mean completion time was 4.9 min (SD 4.4). Internal consistency and intraclass correlation coefficient were adequate (Cronbach α = 0.757 and ICC = 0.90). Factorial analysis showed a unidimensional structure (a single eigenvalue >1, accounting for 39.1% of variance). Significant Spearman's rank correlations between the TOOL and both generic and specific measures were found. The questionnaire was able to discriminate among the Clinical Global Impression - Severity scores (Mann-Whitney U test, <it>P </it>< 0.001).</p> <p>Conclusions</p> <p>The TOOL questionnaire shows appropriate feasibility, reliability, and discriminative performance as a patient-reported outcome. TOOL constitutes a valuable addition to measure the impact of adverse events of antipsychotic drugs from the patient perspective.</p

Allocation of U.S. Biomass Production to Food, Feed, Fiber, Fuel and Exports

This paper analyzes the end uses—food, feed, fiber, fuel, and exports—of biomass production in the U.S. in 1997, 2002, 2007, and 2012. They are also analyzed at the state level in 2012. Biomass production is measured as human appropriation of net primary production (HANPP), an ecological footprint measured as carbon fixed through photosynthesis, derived from data on crop, timber and grazing yields. HANPP was allocated to end uses using publicly available sources from the U.S. Department of Agriculture and internet-based sources publishing data on agricultural trade. HANPP was 717–834 megatons (MT) of carbon per year, which comprised 515–615 MT of crop-based, 105–149 MT timber-based, and 64–76 MT of grazed HANPP. Livestock feed commanded the largest proportion, but decreased from 395 (50%) to 305 MT (42%) of all HANPP and 320 to 240 MT (58–44%) of crop-based HANPP. The proportion allocated to exports was stable at 118–141 MT (17–18%) of total HANPP and 112–133 MT (21–23%) of crop-based HANPP. Biofiber decreased from 141 MT (18%) to 97 MT (13%) of all HANPP. Biofuel increased strongly from 11 MT to 98 MT, from 1% to 14% of all HANPP and 2% to 18% of crop-based HANPP, surpassing food and biofiber by 2012. Direct food commanded 89–105 MT, the lowest proportion at 12–13% of all HANPP, and 17–18% of crop-based HANPP. The highly fertile Midwest and the drought-prone Intermountain West stand out as regions where a very small percentage of biomass is allocated to direct human food. The high proportions of biomass production allocated to nonfood uses is consistent with the tragedy of ecosystem services and commodification of nature frameworks. Reducing these proportions presents opportunities for improving ecosystem services, food security, and human well-being

Performance Analysis of ML-based MTC Traffic Pattern Predictors

Prolonging the lifetime of massive machine-type communication (MTC) networks is key to realizing a sustainable digitized society. Great energy savings can be achieved by accurately predicting MTC traffic followed by properly designed resource allocation mechanisms. However, selecting the proper MTC traffic predictor is not straightforward and depends on accuracy/complexity trade-offs and the specific MTC applications and network characteristics. Remarkably, the related state-of-the-art literature still lacks such debates. Herein, we assess the performance of several machine learning (ML) methods to predict Poisson and quasi-periodic MTC traffic in terms of accuracy and computational cost. Results show that the temporal convolutional network (TCN) outperforms the long-short term memory (LSTM), the gated recurrent units (GRU), and the recurrent neural network (RNN), in that order. For Poisson traffic, the accuracy gap between the predictors is larger than under quasi-periodic traffic. Finally, we show that running a TCN predictor is around three times more costly than other methods, while the training/inference time is the greatest/least.Comment: IEEE Wireless Communications Letters Print ISSN: 2162-2337 Online ISSN: 2162-234