MIMO Antenna System for Modern 5G Handheld Devices with Healthcare and High Rate Delivery

YesIn this work, a new prototype of the eight-element MIMO antenna system for 5G communications, internet of things, and networks has been proposed. This system is based on an H-shaped monopole antenna system that offers 200 MHz bandwidth ranges between 3.4-3.6GHz, and the isolation between any two elements is well below -12dB without using any decoupling structure. The proposed system is designed on a commercially available 0.8mm-thick FR4 substrate. One side of the chassis is used to place the radiating elements, while the copper from the other side is being removed to avoid short-circuiting with other components and devices. This also enables space for other systems, sub-systems, and components. A prototype is fabricated and excellent agreement is observed between the experimental and the computed results. It was found that ECC is 0.2 for any two radiating elements which is consistent with the desirable standards, and channel capacity is 38 bps/Hz which is 2.9 times higher than 4x4 MIMO configuration. In addition, single hand mode and dual hand mode analysis are conducted to understand the operation of the system under such operations and to identify losses and/or changes in the key performance parameters. Based on the results, the proposed antenna system will find its applications in modern 5G handheld devices and internet of things with healthcare and high rate delivery. Besides that, its design simplicity will make it applicable for mass production to be used in industrial demands

Carbon cycle uncertainty in the Alaskan Arctic

Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magnitude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for the Alaskan Arctic from four recent model intercomparison projects – NACP (North American Carbon Program) site and regional syntheses, TRENDY (Trends in net land atmosphere carbon exchanges), and WETCHIMP (Wetland and Wetland CH4 Inter-comparison of Models Project) – we provide a baseline of terrestrial carbon cycle uncertainty, defined as the multi-model standard deviation (o) for each quantity that follows. Mean annual absolute uncertainty was largest for soil carbon (14.0±9.2 kgCm−2), then gross primary production (GPP) (0.22±0.50 kgCm−2 yr−1), ecosystem respiration (Re) (0.23±0.38 kgCm−2 yr−1), net primary production (NPP) (0.14±0.33 kgCm−2 yr−1), autotrophic respiration (Ra) (0.09±0.20 kgCm−2 yr−1), heterotrophic respiration (Rh) (0.14±0.20 kgCm−2 yr−1), net ecosystem exchange (NEE) (−0.01±0.19 kgCm−2 yr−1), and CH4 flux (2.52±4.02 g CH4 m−2 yr−1). There were no consistent spatial patterns in the larger Alaskan Arctic and boreal regional carbon stocks and fluxes, with some models showing NEE for Alaska as a strong carbon sink, others as a strong carbon source, while still others as carbon neutral. Finally, AmeriFlux data are used at two sites in the Alaskan Arctic to evaluate the regional patterns; observed seasonal NEE was captured within multi-model uncertainty. This assessment of carbon cycle uncertainties may be used as a baseline for the improvement of experimental and modeling activities, as well as a reference for future trajectories in carbon cycling with climate change in the Alaskan Arctic and larger boreal region

Erratum: Search for gravitational waves from binary black hole inspiral, merger, and ringdown (Physical Review D - Particles, Fields, Gravitation and Cosmology 2011; 83(12):122005-1-122005-20)

This paper was published online on 6 June 2011 with an omission in the Collaboration author list. S. Dwyer has been added as of 12 April 2012. The Collaboration author list is incorrect in the printed version of the journal.J. Abadie... D. J. Hosken... J. Munch... D. J. Ottaway... P. J. Veitch...et al. (LIGO Scientific Collaboration, VIRGO Collaboration

Erratum: All-sky search for gravitational-wave bursts in the first joint LIGO-GEO-Virgo run (Physical Review D - Particles, Fields, Gravitation and Cosmology - 2010: 81(10) 102001-1-102001-20)

This paper was published online on 5 May 2010 with an omission in the Collaboration author list. S. Dwyer has been added as of 12 April 2012. The Collaboration author list is incorrect in the printed version of the journalJ. Abadie... D. J. Hosken... J. Munch... D. J. Ottaway... P. J. Veitch...et al. (LIGO Scientific Collaboration, VIRGO Collaboration

Can Current Moisture Responses Predict Soil CO2 Efflux Under Altered Precipitation Regimes? A Synthesis of Manipulation Experiments

As a key component of the carbon cycle, soil CO2 efflux (SCE) is being increasingly studied to improve our mechanistic understanding of this important carbon flux. Predicting ecosystem responses to climate change often depends on extrapolation of current relationships between ecosystem processes and their climatic drivers to conditions not yet experienced by the ecosystem. This raises the question to what extent these relationships remain unaltered beyond the current climatic window for which observations are available to constrain the relationships. Here, we evaluate whether current responses of SCE to fluctuations in soil temperature and soil water content can be used to predict SCE under altered rainfall patterns. Of the 58 experiments for which we gathered SCE data, 20 were discarded because either too few data were available, or inconsistencies precluded their incorporation in the analyses. The 38 remaining experiments were used to test the hypothesis that a model parameterized with data from the control plots (using soil temperature and water content as predictor variables) could adequately predict SCE measured in the manipulated treatment. Only for seven of these 38 experiments, this hypothesis was rejected. Importantly, these were the experiments with the most reliable datasets, i.e., those providing high-frequency measurements of SCE. Accordingly, regression tree analysis demonstrated that measurement frequency was crucial; our hypothesis could be rejected only for experiments with measurement intervals of less than 11 days, and was not rejected for any of the 24 experiments with larger measurement intervals. This highlights the importance of high-frequency measurements when studying effects of altered precipitation on SCE, probably because infrequent measurement schemes have insufficient capacity to detect shifts in the climate-dependencies of SCE. We strongly recommend that future experiments focus more strongly on establishing response functions across a broader range of precipitation regimes and soil moisture conditions. Such experiments should make accurate measurements of water availability, they require high-frequency SCE measurements and they should consider both instantaneous responses and the potential legacy effects of climate extremes. This is important, because we demonstrated that at least for some ecosystems, current moisture responses cannot be extrapolated to predict SCE under altered rainfall

Design of EGR Solenoid Valve Controller using Neural Networks

This paper describes an initial investigation into the use of neural network learning algorithms to obtain a controller for a non-linear system over a large operating space within the context of automotive applications. In order to perform a comparative study of the various adaptive systems, the problem of controlling the motion of a solenoid-operated EGR (Exhaust Gas Recirculation) valve is considered. This study also compares a neurocontroller with a PID controller for various position step changes in both directions. During the investigation it was found that the performance of the neurocontroller was consistently better, particularly for large demanded step changes, and that the neurocontroller consistently used less control energy. Further work will focus on why these nonlinear learning systems outperform perform PID controllers in this application

Wydajność plonu ziarna, korelacja i analiza skupień w elitarnych liniach pszenicy (Triticum aestivum L.)

Wheat is a leading cereal, playing a crucial role in feeding the hungry world and improving global food security. The present study was undertaken to comparatively analyze the extent of genetic diversity for various quantitative traits among the wheat material exotic to Pakistan, received from CIMMYT (The International Maize and Wheat Improvement Center), Mexico. Nineteen advanced lines from the Semi-Arid Wheat Yield Trial (SAWYT) were studied along with a local cultivar, considered a control (NIA-Amber). Data were recorded on nine important agro-morphic traits. The compared genotypes differed significantly (p ≤ 0.05) in the studied traits, where line V6 produced the highest mean grain yield (6,049 kg ha−1) and maximum 1,000-grain weight (45.0 g). Other lines, V19, V17, and V2, also showed superiority in yield (5,723, 5,150, and 5,067 kg ha−1, respectively). Days to heading established a significant positive association with days to maturity (r = 0.7995), plant height (r = 0.3168), spike length (r = 0.2696), and spikelets per spike (r = 0.4391). The important yield associated trait, 1,000-grain weight, had a highly significant positive correlation (r = 0.6833) with grain yield. Cluster analysis for various quantitative traits showed important information about genetic diversity for the studied traits among wheat genotypes. Hence, selection of genotypes for higher grain yield based on these traits could be useful for future breeding.Pszenica (Triticum aestivumL.) jest jednym z najważniejszych zbóż, odgrywającym kluczową rolę w zaspokajaniu potrzeb żywnościowych ludności i poprawie globalnego bezpieczeństwa żywnościowego. Niniejsze badania podjęto w celu przeprowadzenia analizy porównawczej stopnia różnorodności genetycznej wybranych cech ilościowych u linii pszenicy egzotycznych dla Pakistanu, otrzymanymi z Meksyku (CIMMYT; The International Corn and Wheat Impro- vement Center). Dziewiętnaście linii pochodzących z Semi-Arid Wheat Yield Trial (SAWYT) badano razem z lokalną odmianą NIA-Amber, przyjętą jako kontrola. Dane rejestrowano dla dziewięciu ważnych cech agro-morfologicznych. Porównywane genotypy różniły się istotnie (p≤ 0,05) pod względem badanych cech. Linia V6 dała najwyższy średni plon ziarna (6049 kg ha−1) i największą masę 1000 ziarniaków (45,0 g). Linie V19, V17 i V2 również wykazały wysoki plon (odpowiednio 5723, 5150 i 5067 kg ha−1). Liczba dni do kłoszenia była istotnie dodatnio skorelowana z wysokością roślin (r= 0,7995), dniami do dojrzałości (r= 0,3168), długością kłosa ( r= 2696) i liczbą kłosków w kłosie (r= 0,4391). Ważna cecha wpływająca na plon – masa 1000 ziarniaków – była wysoce dodatnio skorelowana z plonem ziarna (r= 0,6833). Analiza skupień dla różnych cech ilościowych dostarczyła ważnych informacji o różnorodności genetycznej badanych cech między genotypami pszenicy, dlatego też wybór genotypów dla uzyskania wyższego ziarna w oparciu o te cechy może być przydatny do przyszłej hodowli

Action Planning for the collision Avoidance System Using Neural Networks

An understanding of the scenario in complex traffic situations is essential in order to give an early warning, or in an autonomous system, to intervene in the urban or motorway environment. A collision avoidance system needs both to predict possible collisions or hazards and to plan a less hazardous move in a critical situation. A crucial factor in the success of the system is the use of a priori knowledge. The classical problem with a knowledge-based decision making system is the acquisition and representation of the knowledge. It is difficult to design and develop a system for real time auto-piloting in varied traffic environments. Neural networks are ideally suited for applications where a large training set is available because they can apply human decision making criteria in different situations. The learning processes encapsulate a wide variety of drivers' reactions to various scenarios. Neural networks' abilities to generalise their training to new scenarios in the light of driving experience and to make emotion-free decisions leads to a system that is adaptive and closely which resembles human action strategy. Recognition of a scenario is achieved by acquiring data about a scene from a variety of sensors. Visual data is preprocessed and features are extracted using a real-time image processing system, while microwave radar provides obstacle information and distances. This paper described an early warning system and suggests possible responses to various traffic situations. The paper focuses on various learning algorithms for decision making which is based on the current model and immediate history only. It would help if we could always recognise the dominant threat at every instant and avoid it by either slowing down or changing direction. In our analysis of situations using neural networks, the test cases show that reasonably such behaviour can be generated. In order to validate the auto pilot it is tested in parallel with expert drivers to assess the drivers' action in a number of scenarios. The network's intervention control is verified by independent observers. The intervention strategies are based on a number of rules by which an intervention controller is trained to generate various actions. These rules are fine tuned on-line to achieve reliable and repeatable actions

Heat and drought impact on carbon exchange in an age-sequence of temperate pine forests

Background: Most North American temperate forests are plantation or regrowth forests, which are actively managed. These forests are in different stages of their growth cycles and their ability to sequester atmospheric carbon is affected by extreme weather events. In this study, the impact of heat and drought events on carbon sequestration in an age-sequence (80, 45, and 17 years as of 2019) of eastern white pine (Pinus strobus L.) forests in southern Ontario, Canada was examined using eddy covariance flux measurements from 2003 to 2019. Results: Over the 17-year study period, the mean annual values of net ecosystem productivity (NEP) were 180 ± 96, 538 ± 177 and 64 ± 165 g C m–2 yr–1 in the 80-, 45- and 17-year-old stands, respectively, with the highest annual carbon sequestration rate observed in the 45-year-old stand. We found that air temperature (Ta) was the dominant control on NEP in all three different-aged stands and drought, which was a limiting factor for both gross ecosystem productivity (GEP) and ecosystems respiration (RE), had a smaller impact on NEP. However, the simultaneous occurrence of heat and drought events during the early growing seasons or over the consecutive years had a significant negative impact on annual NEP in all three forests. We observed a similar trend of NEP decline in all three stands over three consecutive years that experienced extreme weather events, with 2016 being a hot and dry, 2017 being a dry, and 2018 being a hot year. The youngest stand became a net source of carbon for all three of these years and the oldest stand became a small source of carbon for the first time in 2018 since observations started in 2003. However, in 2019, all three stands reverted to annual net carbon sinks. Conclusions: Our study results indicate that the timing, frequency and concurrent or consecutive occurrence of extreme weather events may have significant implications for carbon sequestration in temperate conifer forests in Eastern North America. This study is one of few globally available to provide long-term observational data on carbon exchanges in different-aged temperate plantation forests. It highlights interannual variability in carbon fluxes and enhances our understanding of the responses of these forest ecosystems to extreme weather events. Study results will help in developing climate resilient and sustainable forestry practices to offset atmospheric greenhouse gas emissions and improving simulation of carbon exchange processes in terrestrial ecosystem models. © 2022, The Author(s).Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]