10 research outputs found
Soft computing approaches based bookmark selection and clustering techniques for social tagging systems
No full textAIRank: Author Impact Ranking through Positions in Collaboration Networks
No full textCitation is a universally acknowledged way for scientific impact evaluation. However, due to its easy manipulability, simply relying on citation cannot objectively reflect the actual impact of scholars. Instead of citation, we utilize the academic networks, in virtue of their available and abundant academic information, to evaluate the scientific impact of scholars in this paper. Through the collaboration among scholars in academic networks, we notice an interesting phenomenon that scholars in some special positions can access more kinds of information and connect researchers from different groups to promote the scientific collaborations. However, this important fact is generally ignored by the existing approaches. Motivated by the observations above, we propose the novel method AIRank to evaluate the scientific impact of scholars. Our method not only considers the impact of scholars through the mutual reinforcement process in heterogeneous academic networks, but also integrates the structural holes theory and information entropy theory to depict the benefit that scholars obtain via their positions in the network. The experimental results demonstrate the effectiveness of AIRank in evaluating the impact of scholars more comprehensively and finding more top ranking scholars with interdisciplinary nature
Generic 5G NR LDPC Encoder Architecture Optimized for Area and Throughput
No full textPhysical Downlink Shared Channel (PDSCH) in 5G New Radio (NR) uses LDPC codes as the channel coding solution for their efficient error-correcting performance and suitability for high-speed communications. To meet the high-throughput requirements of the 5G NR technology, this paper discusses different 5G NR LDPC encoder architectures that enable different parallel encoding schemes and optimizes the design for specific metrics. The recent architectural designs might lack effective design metrics that ensure high throughput, low area and gate counts, and full compatibility with the 5G NR standard. The suggested architecture aims to a generic design with flexible controller that is fully compatible with all the supported code block sizes and code rates in the 5G NR standard. The design is implemented in ASIC using NanGate-15 nm standard cells CMOS technology with The Cadence Genus synthesis solution. The proposed architecture targets high-throughput encoding operations with a low-area hardware design. The synthesis of the suggested encoder resulted in a maximum frequency of 1.71 GHz and gate counts of 491.8 K gates with all the code block sizes and code rates in 5G NR standard supported. For the largest code length, the proposed architecture’s throughput is up to 451.44 Gbps. Among the discussed previous works, there is one that targets a very high throughput of 257.9 Gbps but implemented using very high gate counts of 1126 K gates for only one code-word size (25344, 8448). This previous work implements encoding operation in a sub-matrix-by-sub-matrix encoding scheme. To balance between gate counts and throughput, another previous encoder architecture work, designed for only one code-word size (23232, 7744), achieved a relatively high throughput of 202.4 Gbps and gate counts of 486.4 K gates. This architecture conducts parallel encoding operation in a row-by-row scheme. The results confirmed that the proposed architecture achieves suitable balance between high throughput, gate counts, and 5G NR compatibility compared to the previous works. The postsynthesis results showed a throughput improvement of 123% and 75.04% compared to row-by-row and submatrix-based schemes, respectively. They also show a gate count reduction of 56% and 23.1% compared to submatrix-based and column-based schemes, respectively
Selection of appropriate time scale with Boruta algorithm for regional drought monitoring using multi-scaler drought index
No full textSpatial distribution of drought plays key role specifically in hydrological research. Drought is a complex stochastic natural hazard caused by prolonging shortage of rainfall and available water resources. The multi-scalar drought indices (based on probability distribution) are commonly used for making effective drought mitigation policies. In addition, the multi-scalar drought indices have great extent of the inaccurate determination of drought classes due to its probabilistic nature. However, the interpretation and applicability of various time scales are cumbersome for multi-scalar drought in various meteorological stations at a particular region. In this regards, accurate estimation and continuous monitoring of future drought at regional level requires a more appropriate and important time scale with respect to regions under study. In this study, we aimed to investigate the appropriate time scale for multi-scalar drought indices by using geo-reference points of meteorological stations. We used Boruta algorithm with two random forest adapters of machine learning algorithms for regionalized optimization of drought monitoring time scale. We explored the appropriate time scale for the Standardized Precipitation Temperature Index (SPTI) of 52 meteorological stations that are located across Pakistan. Results show that the significant importance of SPTI-1 (1-month time scale) for further spatial and temporal studies. That is, being high ranked and prominence, SPTI-1 has the ability to capture the characteristics of all other time scales that are in some circumstances applicable for drought characterization and classification
Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions
No full textThe impact of air-railways transportation, energy demand, bilateral aid flows, and population density on environmental degradation: Evidence from a panel of next-11 countries
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A new spatiotemporal two-stage standardized weighted procedure for regional drought analysis
Get PDFDrought is a complex phenomenon that occurs due to insufficient precipitation. It does not have immediate effects, but sustained drought can affect the hydrological, agriculture, economic sectors of the country. Therefore, there is a need for efficient methods and techniques that properly determine drought and its effects. Considering the significance and importance of drought monitoring methodologies, a new drought assessment procedure is proposed in the current study, known as the Maximum Spatio-Temporal Two-Stage Standardized Weighted Index (MSTTSSWI). The proposed MSTTSSWI is based on the weighting scheme, known as the Spatio-Temporal Two-Stage Standardized Weighting Scheme (STTSSWS). The potential of the weighting scheme is based on the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and the steady-state probabilities. Further, the STTSSWS computes spatiotemporal weights in two stages for various drought categories and stations. In the first stage of the STTSSWS, the SPI, SPEI, and the steady-state probabilities are calculated for each station at a 1-month time scale to assign weights for varying drought categories. However, in the second stage, these weights are further propagated based on spatiotemporal characteristics to obtain new weights for the various drought categories in the selected region. The STTSSWS is applied to the six meteorological stations of the Northern area, Pakistan. Moreover, the spatiotemporal weights obtained from STTSSWS are used to calculate MSTTSSWI for regional drought characterization. The MSTTSSWI may accurately provide regional spatiotemporal characteristics for the drought in the selected region and motivates researchers and policymakers to use the more comprehensive and accurate spatiotemporal characterization of drought in the selected region.Validerad;2022;Nivå 2;2022-05-02 (joosat);Funder: Deanship of Scientific Research at King Saud University (1435-075)</p
Measuring the ecological footprint of inbound and outbound tourists: Evidence from a panel of 35 countries
Get PDFThe ecological footprint of tourism is imperative to assess for United Nation’s environmental sustainable agenda that is provoked for healthy visitation of tourists without damaging natural environment. This would ultimately reap economic and environmental benefts to sustained international tourism. This study examined the relationship between international tourism indicators, air pollutants, and ecological biodiversity underlying the premises of environmental Kuznets curve in the panel of 35 tourists-induced countries for the period of 1995–2016. The study used panel fxed efect and panel twostage least square regression technique for robust inferences. The results confrmed the following key points, i.e., (1) the U-shaped relationship found between inbound tourists and mono-nitrogen oxide (NOx), where inbound tourists initially do not emanate the NOx emissions, while at the later stages, the level of NOx emissions substantially raises the required strong policy intervention to reduce emissions and provide tourists safe and healthy destinations, (2) inbound tourists linked with the biodiversity loss, and it increases carbon dioxide (CO2) emissions and greenhouse gas (GHG) emissions in a panel of potential habitat area, while it decreases NOx and SO2 emissions, (4) international tourists’ departure exercised the ‘rebound efect’ on the ecosystem and air pollutants across countries, (5) there is a monotonic increasing relationship between outbound tourists and ecological footprint, while there is a fat/no relationship between outbound tourists, NOx, CO2, SO2, and GHG emissions, and (6) the food management practices supported the ecological diversity, and it reduces the carbon ‘foodprint,’ while it substantially increases SO2 emissions in outbound tourists’ model. The study emphasized the need for sustainable tourism infrastructure that conserves our natural environment and reduces climatic variability across the globe
