Search

Convolutional neural networks (CNNs) are essential for image classification and detection, and their implementation in embedded systems is becoming increasingly attractive due to their compact size and low power consumption. Field-Programmable Gate Arrays (FPGAs) have emerged as a promising option, thanks to their low latency and high energy efficiency. Vitis AI and FINN are two development environments that automate the implementation of CNNs on FPGAs. Vitis AI uses a deep learning processing unit (DPU) and memory accelerators, while FINN is based on a streaming architecture and fine-tunes parallelization. Both environments implement parameter quantization techniques to reduce memory usage. This work extends previous comparisons by evaluating both environments by implementing four models with different numbers of layers on the Xilinx Kria KV260 FPGA platform. The complete process from training to evaluation on FPGA, including quantization and hardware implementation, is described in detail. The results show that FINN provides lower latency, higher throughput, and better energy efficiency than Vitis AI. However, Vitis AI stands out for its simplicity in model training and ease of implementation on FPGA. The main finding of this study is that as the complexity of the models increases (with more layers in the neural networks), the differences in terms of performance and energy efficiency between FINN and Vitis AI are significantly reduced.

With the advancement in artificial intelligence technology, autonomous mobile robots have been utilized in various applications. In autonomous driving scenarios, object classification is essential for robot navigation. To perform this task, light detection and ranging (LiDAR) sensors, which can obtain depth and height information and have higher resolution than radio detection and ranging (radar) sensors, are preferred over camera sensors. The pillar-based method employs a pillar feature encoder (PFE) to encode 3D LiDAR point clouds into 2D images, enabling high-speed inference using 2D convolutional neural networks. Although the pillar-based method is employed to ensure real-time responsiveness of autonomous driving systems, research on accelerating the PFE is not actively being conducted, although the PFE consumes a significant amount of computation time within the system. Therefore, this paper proposes a PFE hardware accelerator and pillar-based object classification model for autonomous mobile robots. The proposed object classification model was trained and tested using 2971 datasets comprising eight classes, achieving a classification accuracy of 94.3%. The PFE hardware accelerator was implemented in a field-programmable gate array (FPGA) through a register-transfer level design, which achieved a 40 times speedup compared with the firmware for the ARM Cortex-A53 microprocessor unit; the object classification network was implemented in the FPGA using the FINN framework. By integrating the PFE and object classification network, we implemented a real-time pillar-based object classification acceleration system on an FPGA with a latency of 6.41 ms. [ABSTRACT FROM AUTHOR]

Field-programmable gate array (FPGA)--based accelerators are becoming increasingly popular for deep neural network (DNN) inference due to their ability to scale performance with increasing degrees of specialization with dataflow architectures or custom data type precision. In order to reduce the barrier for software engineers and data scientists to adopt FPGAs, C++- and OpenCL-based design entries with high-level synthesis (HLS) have been introduced. They provide higher abstraction compared with register-transfer level (RTL)--based design. HLS offers faster development time, better maintainability, and more flexibility in code exploration when evaluating several options for multi-dimension tensors, convolutional layers, or different degrees of parallelism. For this reason, HLS has been adopted by DNN accelerator generation frameworks such as FINN and hls4ml. In this article, we present an alternative backend library for FINN, leveraging RTL. We investigate and evaluate, across a spectrum of design dimensions, the pros and cons of an RTL-based implementation versus the original HLS variant. We show that for smaller design parameters, RTL produces significantly smaller circuits as compared with HLS. For larger circuits, however, the look-up table (LUT) count of RTL-based design is slightly higher, up to around 15%. On the other hand, HLS consistently requires more flip-flops (FFs; with an orders-of-magnitude difference for smaller designs) and block RAMs (BRAMs; 2x more). This also impacts the critical path delay, with RTL producing significantly faster circuits, up to around 80%. RTL also benefits from at least a 10x reduction in synthesis time. Finally, the results were validated in practice using two real-world use cases, one of a multi-layer perceptron (MLP) used in network intrusion detection and the other a convolution network called ResNet, used in image recognition. Overall, since HLS frameworks code-generate the hardware design, the benefits of the ease in the design entry is less important. As such, the gained benefits in synthesis time together with some design-dependent resource benefits make the RTL abstraction an attractive alternative. [ABSTRACT FROM AUTHOR]

In 2019 there were forest fires in Sumatra, Indonesia, which co-occurred with a strong positive IOD. The forest fire disaster caused the thick smoke containing dusts such as particulate matter with a size of 2.5μm (PM2.5). In this study, a model simulation was conducted to predict the distribution of PM2.5 using the WRF–Chem model when forest fires in 2019 occurred. The prediction was produced by adding the latest version of fire inventory from NCAR (FINN) input data, version 2.5. The prediction result was verified and compared with ground station data from the Ministry of Environment and Forestry (KLHK) station and NASA’s EOSDIS satellite imagery. There are three ground stations that were used for verification: the Jambi, Palembang and Pekanbaru stations. Of the three stations, the prediction results at the Palembang station are the best in correlation and RMSE value. Spatially, the distribution of PM2.5 from the model result is similar and can follow the pattern of smoke distribution from satellite imagery observations. Even though, generally the WRF–Chem model equipped with the latest FINN data still cannot produce an accurate prediction for the 2019 forest fires event yet in Sumatra region.

Prescribed burning is a major source of a fine particular matter, especially in the southeastern United States, and quantifying emissions from burning operations accurately is an integral part of ascertaining air quality impacts. For instance, a critical factor in calculating fire emissions is identifying fire activity information (e.g., location, date/time, fire type, and area burned) and prior estimations of prescribed fire activity used for calculating emissions have either used burn permit records or satellite-based remote sensing products. While burn permit records kept by state agencies are a reliable source, they are not always available or readily accessible. Satellite-based remote sensing products are currently used to fill the data gaps, especially in regional studies; however, they cannot differentiate prescribed burns from the other types of fires. In this study, we developed novel algorithms to distinguish prescribed burns from wildfires and agricultural burns in a satellite-derived product, Fire INventory from NCAR (FINN). We matched and compared the burned areas from permit records and FINN at various spatial scales: individual fire level, 4 km grid level, and state level. The methods developed in this study are readily usable for differentiating burn type, matching and comparing the burned area between two datasets at various resolutions, and estimating prescribed burn emissions. The results showed that burned areas from permits and FINN have a weak correlation at the individual fire level, while the correlation is much higher for the 4 km grid and state levels. Since matching at the 4 km grid level showed a relatively higher correlation and chemical transport models typically use grid-based emissions, we used the linear regression relationship between FINN and permit burned areas at the grid level to adjust FINN burned areas. This adjustment resulted in a reduction in FINN-burned areas by 34%. The adjusted burned area was then used as input to the BlueSky Smoke Modeling Framework to provide long-term, three-dimensional prescribed burning emissions for the southeastern United States. In this study, we also compared emissions from different methods (FINN or BlueSky) and different data sources (adjusted FINN or permits) to evaluate uncertainties of our emission estimation. The comparison results showed the impacts of the burned area, method, and data source on prescribed burning emission estimations. [ABSTRACT FROM AUTHOR]

In the illustration accompanying ‘!CEÓL!’ (!Music!), Brian Ua Nualláin’s 1932 Irish-language story about radio and gramophone music, Ua Nualláin uses an established but outdated spelling of ‘ceol’ to suggest a polysemantic pun. This pun is significant given the story’s subtitle – ‘Eachtra an Fhir Ólta’ (The Tale of the Drunkard) – because it places ‘ól,’ the Irish word for drink, inside the word for music, ‘ceol.’ The resulting portmanteau in Ua Nualláin’s illustration is interfusional in Thomas King’s sense, because it blends the textual with the oral. The orthography Ua Nualláin uses for his visual representation of ‘!CEÓL!’ draws attention to how the word is spoken, and the stress on the second half of the word makes it sound drunken. We argue that the story’s implied title, ‘Drink-Music,’ thematises intertextual play in ways that anticipate Ua Nualláin’s Middle Irish Joycean pastiche ‘Pisa bec oc Parnabus’ (1935/1938). This note provides the first critical discussion of the ‘!CEÓL!’ illustration, observing the presence of Ua Nualláin’s initials in the bottom right-hand corner of the image.

This study analyzed the characteristics of changes in O3 concentration in a plume induced by a wildfire in Andong, South Korea, from 24 to 26 April 2020, using the Community Multi-scale Air Quality (CMAQ) model. Fire INventory from National Center for Atmospheric Research (FINN) emissions data were used for the wildfire emissions. The increases in the concentrations of primary pollutants (CO, NOx, and volatile organic compounds (VOCs)) due to the wildfire peaked near the source at 09 LST and, as the plume was transported, the reduction in the supply of pollutants from wildfire, as well as chemical reactions, advection and diffusion, and deposition, caused the concentrations to continuously decrease. In contrast, O3 concentration showed a sustained increase during transport due to photochemical reactions caused by precursors (e.g., NOx, VOCs) emitted during the wildfire, peaking (1.40 ppb) at approximately 1 km at 13 LST over 60 km from the source. To analyze these results, a process analysis was conducted. Integrated process rate (IPR) analysis results showed that the production rate of O3 and loss rates of NOx and VOCs peaked at 09 LST due to the photochemical reactions of NOx and VOCs emitted due to wildfire. Then, as the plume was transported, the loss rates of NOx and VOCs that contributed to O3 production continued to decrease at 11 LST. The O3 production rate also decreased at 11 LST but increased at 13 LST due to increasing solar radiation. This indicates that the O3 concentration is complexly determined by O3 precursors and solar radiation. Additionally, IRR analysis revealed that NO and NO2 emitted during wildfire and solar radiation contributed to the production and loss processes of O3; the production reactions of O3 were predominant, and O3 was accumulated and transported in the plume, leading to the peak O3 concentration at 13 LST. [ABSTRACT FROM AUTHOR]

Object detection is an essential component of many systems used, for example, in advanced driver assistance systems (ADAS) or advanced video surveillance systems (AVSS). Currently, the highest detection accuracy is achieved by solutions using deep convolutional neural networks (DCNN). Unfortunately, these come at the cost of a high computational complexity; hence, the work on the widely understood acceleration of these algorithms is very important and timely. In this work, we compare three different DCNN hardware accelerator implementation methods: coarse-grained (a custom accelerator called LittleNet), fine-grained (FINN) and sequential (Vitis AI). We evaluate the approaches in terms of object detection accuracy, throughput and energy usage on the VOT and VTB datasets. We also present the limitations of each of the methods considered. We describe the whole process of DNNs implementation, including architecture design, training, quantisation and hardware implementation. We used two custom DNN architectures to obtain a higher accuracy, higher throughput and lower energy consumption. The first was implemented in SystemVerilog and the second with the FINN tool from AMD Xilinx. Next, both approaches were compared with the Vitis AI tool from AMD Xilinx. The final implementations were tested on the Avnet Ultra96-V2 development board with the Zynq UltraScale+ MPSoC ZCU3EG device. For two different DNNs architectures, we achieved a throughput of 196 fps for our custom accelerator and 111 fps for FINN. The same networks implemented with Vitis AI achieved 123.3 fps and 53.3 fps, respectively. [ABSTRACT FROM AUTHOR]

Fluidization mechanism of liquefiable cargo is similar to that of sand liquefaction. But the cargo is subjected to more complex external loads during sea transportation. Based on two different constitutive models, numerical predictions were conducted in this study. And the effects of ship motions including motion acceleration, frequency and relative density of cargo on fluidization was investigated. By comparing with available the experimental data, validation is carried out. Results show that the roll motion is the most important one related to cargo fluidization. When the motion acceleration increases, the possibility of cargo fluidization increases. The higher of the cargo density the lower of the possibility of cargo fluidization. The effect of frequency on cargo fluidization is not unique, and it exists a critical value. The fluidization behavior of cargo could be described both by UBCSAND and FINN models. And the comparisons were discussed and summed up. [ABSTRACT FROM AUTHOR]

At the end of the dry season, March and April in Southeast Asia (SEA), agricultural refuse burnings occur over the region, mainly in the countries of Myanmar, Thailand, Laos, Cambodia and Vietnam, in preparation for the wet rice plantation. In this study, the impact of biomass burnings at the height of the burning period in March 2019 in mainland SEA on air quality and pollutant transport is modelled using the Weather Research Forecast WRF-Chem air quality model with emission input from the National Center for Atmospheric Research (NCAR) Fire Emission Inventory from NCAR (FINN). FINN is derived from satellite remote sensing data and species emission factors. A simulation of the dispersion of pollutants from biomass burnings from 13 to 19 March 2019, when the burnings was most intense, was performed. Validation of the model prediction using observed meteorological and pollutant data such as AOD measurements on ground from AERONET (Aerosol Robotic Network) and data from MODIS and CALIPSO satellites is carried out at various sites in the region. The results show that impact on air quality was most pronounced in Thailand and Laos but the effect of biomass burnings in mainland SEA at the end of the dry season is widespread in terms of pollutant dispersion and population exposure over the whole region and beyond. It is also shown that the transport of pollutants from biomass burnings in SEA to southern China, Taiwan and beyond is facilitated by the Truong Son mountain range, when under westerly wind, acting as a launching pad to uplift the pollutant plumes to higher altitude which then can be dispersed widely and transported farther from the biomass burning sources in Thailand and Laos. [ABSTRACT FROM AUTHOR]

The substantial amount of pollutants produced by crop burning leads to particulate matter (PM 2.5) formation through chemical reactions in the atmosphere. In this study, we quantitatively examined the characteristics of PM 2.5 component concentration changes and the production mechanism of sulfate (SO 4 2−) due to crop burning in China, during a period of high PM 2.5 concentrations in South Korea. Crop burning emissions were obtained from the Fire INventory from NCAR (FINN), and simulations were conducted using the Community Multi-scale Air Quality (CMAQ) model. The concentrations of PM 2.5 , nitrate, ammonium, organic carbon, and elemental carbon caused by crop burning were the highest in Northeast China, while the SO 4 2− concentration was higher in the Shandong Peninsula and the Yellow Sea. SO 4 2− was mainly produced through the oxidation reaction by hydrogen peroxide (H 2 O 2) (H 2 O 2 + S(IV) = S(VI) + H 2 O) (1.44 μg/m3, 80.49%). H 2 O 2 that contributed to SO 4 2− production was primarily produced through the self-reaction of hydroperoxy radical (HO 2). Moreover, HO 2 , which contributed to the production of H 2 O 2 , was mainly formed through the decomposition of peroxynitric acid (PNA), which is produced by nitric oxide (NO x) emissions from crop burning. SO 4 2− in the Yellow Sea was reported to be predominantly produced by the oxidation of sulfur dioxide (SO 2) by hydroxyl radical (OH) (OH + SO 2 = SULF + HO 2); however, our study suggests that the PNA formed by NO x emissions from crop burning could contribute to an increase in H 2 O 2 , which subsequently promotes SO 4 2− production. The produced SO 4 2− was then transported across the Yellow Sea, impacting the elevated PM 2.5 concentrations in Jeju Island, a suburban area of South Korea. These findings demonstrate that the mechanisms of SO 4 2− production by crop burning emissions differ from those of anthropogenic emissions. [Display omitted] • Characteristics of transboundary transport of SO 4 2− concentrations induced by crop burning were investigated. • The SO 4 2− due to crop burning was primarily produced by the reaction of H 2 O 2 with S(IV), differing from that by anthropogenic emissions. • The H 2 O 2 was produced through the self-reaction of HO 2 , which was formed by the decomposition reaction of PNA. • The transported SO 4 2− contributed to an increase in the proportion of SO 4 2− in the PM 2.5 concentrations in South Korea. [ABSTRACT FROM AUTHOR]

Object detection is an essential component of many systems used, for example, in advanced driver assistance systems (ADAS) or advanced video surveillance systems (AVSS). Currently, the highest detection accuracy is achieved by solutions using deep convolutional neural networks (DCNN). Unfortunately, these come at the cost of a high computational complexity; hence, the work on the widely understood acceleration of these algorithms is very important and timely. In this work, we compare three different DCNN hardware accelerator implementation methods: coarse-grained (a custom accelerator called LittleNet), fine-grained (FINN) and sequential (Vitis AI). We evaluate the approaches in terms of object detection accuracy, throughput and energy usage on the VOT and VTB datasets. We also present the limitations of each of the methods considered. We describe the whole process of DNNs implementation, including architecture design, training, quantisation and hardware implementation. We used two custom DNN architectures to obtain a higher accuracy, higher throughput and lower energy consumption. The first was implemented in SystemVerilog and the second with the FINN tool from AMD Xilinx. Next, both approaches were compared with the Vitis AI tool from AMD Xilinx. The final implementations were tested on the Avnet Ultra96-V2 development board with the Zynq UltraScale+ MPSoC ZCU3EG device. For two different DNNs architectures, we achieved a throughput of 196 fps for our custom accelerator and 111 fps for FINN. The same networks implemented with Vitis AI achieved 123.3 fps and 53.3 fps, respectively.

Copyright of Lähivõrdlusi / Lähivertailuja is the property of Eesti Rakenduslingvistika Uhing (ERU) / Estonian Association for Applied Linguistics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The planned incorporation into the Polish legal system in the near future and, consequently, the introduction of Companies Investing in Real Estate Leasing or Rental (FINN) into the Polish capital market will result in an institutional change within the financial system. Similarly to closed-ended investment funds and real estate joint-stock companies, the FINN companies will specialize in investing capital in the residential real estate market, and later perhaps also in the commercial real estate market. The principles of FINN's activities included in the legislative draft are modelled directly on the legal regime of the Real Estate Investment Trust (REIT), therefore, they will be subject to numerous prohibitions and limitations, introducing organizational controls and restrictions to the investments, which however is rationally justified. These procedures aim at creating an entity with a structure of a relatively neutral, passive real estate broker or intermediary, focused solely on generating benefits for investors-shareholders. The initiation of the FINN market in Poland, and thus the scale of the ensuing institutional change, will be determined by the inclination of joint stock companies from the WIGNieruchomości index (Warsaw Stock Exchange Real Estate Index) to transform into FINN companies. In turn, this inclination will be determined by the conditions of institutional change - mostly of a legislative and market-driven character. With this in mind, the aim of this study is to conduct a preliminary analysis of the conditions of institutional change and present a forecast of the development potential of the WIG-Nieruchomości companies, which, due to their very similar scope and form of activity, constitute the main functional basis for the emergence of the FINN market as a new, separate segment of the capital market in Poland. [ABSTRACT FROM AUTHOR]

The aim is to trace how the ethnonym Kven and the interrelated imagination of Kvenland changed over time in Nordic political discourse from the Viking Age to the mid-eighteenth century. In the negotiations over fixed borders between Sweden, Denmark and Russia, recognition of ethnic groups played an important political role in legitimating the territorial claims of the states. It brought the history of ethnic groups to the table and in the process made visible ethnonyms and names for provinces used previously. The continuity of the ethnonyms is investigated as a chronological chain of communicative and collective memory. The ethnonym and the territory of Kvenland were used by the Norwegians to maintain an ethnic boundary with the Finnish speakers in the upper Bothnian area. The names Kven and Kvenland were never used in Sweden. The investigation shows that the Kvens constituted a group of Finnish speaking people existing in continuity from the Viking Age. Their core territory was situated in the upper Gulf of Bothnia area. When this was integrated into the Swedish kingdom the inhabitants were designated Finns by the Swedes. The Finnish speakers in Tornedalen, thus, kept their linguistic and cultural continuity but lost their western Scandinavian ethnonym Kven. [ABSTRACT FROM AUTHOR]

The Norwegian ethnonym Kven and the Finnish ethnonym Kainulainen occurred at latest in the first millennium AD. A tacit truth held today is that the ethnonyms represent the same ancient Finnish-speaking group, only named differently by Norwegians and Finns. The aim of the article is to find out whether the ethnonyms have been used to designate different groups of people. The Finnish-speakers in the nearby Tornedalen has called the lower part of the Kalix River in northernmost Sweden the Kainuu River and the upper part Kaalas River after the original Sámi name of the river. According to theories on ethnicity they called the lower part the Kainuu River [Fin. Kainuunväylä] because they wanted to mark out the Swedish speakers of different ethnicity, who they called Kainulaiset. The latter mainly settled the lower part of the river in the Middle Ages and Finnish-speakers the upper part. The article reveals that the Sámi variety Gainolâš was used by the Sámi for depicting dominant majority populations of different ethnicity, especially Scandinavians, but sometimes also Finns. It also argues that Finnish settlers in southern Finland and the northernmost Gulf of Bothnia used Kainulainen for depicting Swedish settlers when the two language groups first encountered. [ABSTRACT FROM AUTHOR]

The Force Awakens enables, even fosters comparisons: is Rey a worthy hero? Is Finn believable as a once-Stormtrooper turned good resistance fighter? Is Kylo Ren anywhere close in power and ability to his role model, Darth Vader? Readers can glean my opinion from that evaluative sentence. Are the scenes parallel to earlier movies? This movie, the new characters, the new poster, the toys, the novelization (by Alan Dean Foster, the science fiction writer and film novelization specialist who served as ghost writer for George Lucas’ Star Wars: From the Adventures of Luke Skywalker, 1976, and Star Wars: The Force Awakens, 2016), and the countless viewing parties, the information sites such as Wookieepedia, and wonderful spoofs such as YouTube’s Bad Lip Reading enable me to say that this film is far beyond simple nostalgia and money-making scheme—though both of those are in play here. It is not the completeness and closure of The Force Awakens that compelled the editors to select that movie and those experiences for a special issue. Instead, it is the excitement that this jump-start has created. The structure at the center of the Star Wars universe has held, and the future looks bright for more new characters and also more wars. The battle for political and economic control on Jakku has something to do with our current Earth-bound problems about inequality and migration and war. But more locally, for Issue 12, questions about identity come to the fore: Who gets to create new Star Wars characters? Who gets to play those new characters? Who gets to play with storylines? Who gets to be the hero and villain? Who gets to voice opinions about the new shape of the First Order? These questions lead to “how” questions—especially how should new viewers and readers understand The Force Awakens. These questions motivate the four articles and one interview in this special issue.

Persistent and intensive wildland dense-fires (DFs) release substantial amounts of airborne pollutants, resulting in a sharp increase in emissions and leading to serious impacts on the environment and human health over extensive geographical areas. It is challenging to thoroughly investigate patterns of fire occurrence and fire distribution for predicting wildfire behaviour, and it is especially difficult to distinguish the characteristics of human-caused and climate-driven fires. Here, we identify and assess dense-fire (DF) from the perspective of spatiotemporally integrated processes using a machine-learning method based on a density-based clustering algorithm with noise constraint ratio. DFs represent collections of fires with homogenous behaviour and therefore allow the study of their internal features, which can reveal fixed patterns of fire occurrence and distribution as well as the evolution of fires over time. We estimated and labelled thousands of fire clusters on the Indochina Peninsula between 2010 and 2020, most of which occurred between December and May. For large-scale DFs, the number of fires contained and amount of atmospheric pollutants emitted were accounted for throughout most of the region, and the time, location and scale of their occurrence each year were relatively stable and predictable. Furthermore, the results of a secondary cluster analysis of fire interactions over the past decade showed two extreme fire events, labelled"north" and"south" groups, whose activities significantly impacted the atmospheric environment of the Indochina Peninsula. Additionally, we predicted their start/end dates and daily emissions. The study also found that the recurrence of high-density fires and the correlation between the DF edge and administrative border suggested a positive anthropogenic influence. To the authors' knowledge, this study is the first to analyze fires in a spatiotemporal Euclidean space by using density-based clustering, with high-density fires as independent subjects to study fire behaviour. The method proposed in this study can provide a reference for wildfire prediction and emission forecasting and fire control work. [Display omitted] • Propose a new concept for studying high-density fires based on density algorithm. • Indicate the predictability of large-scale dense-fire distributions and emissions. • Anthropogenic influence is one of the important factors in fire distribution. • Two large-scale intensive fires dominate the emissions in the Indochina Peninsula. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of the History of Sport is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of the History of Sport is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)