| 1. |
- Abbafati, Cristiana, et al.
(författare)
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- 2020
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Doorandishan, Mina, et al.
(författare)
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Molecular docking and simulation studies of a novel labdane type-diterpene from Moluccella aucheri Scheen (Syn. Otostegia aucheri) as human-AChE inhibitor
- 2021
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Ingår i: Journal of Molecular Structure. - : Elsevier. - 0022-2860 .- 1872-8014. ; 1245
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Tidskriftsartikel (refereegranskat)abstract
- Moluccella aucheri Scheen belongs to Lamiaceae family and is native to Iran and Pakistan. Different chromatographic and spectroscopic analyses of a dichloromethane extract of M. aucheri's aerial parts led to the isolation of five phytochemicals. They include an undescribed labdane typediterpene; 6 beta-acetoxy-9 alpha-hydroxy-7-oxa-labd-13-ene-15, 16-olide or moluccelactone (1), and four known compounds: stigmasterol and beta-sitosterol (2, 3), 5-hydroxy-7, 4'-dimethoxyflavone (4) and genkwanin (5). Their chemical structures were determined by spectroscopic data. As a best result, these compounds are firstly reported in this genus and plant. The alpha-glucosidase and acetylcholine esterase (AChE) inhibitory activity of 1 were evaluated by the protein-ligand docking and molecular dynamics studies. Compound 1 exhibited strong binding affinities towards the active site residues of human-AChE (hAChE) enzyme, while it was not potent against alpha-glucosidase enzyme. In conclusion, compound 1 is suggested as a potential natural inhibitor of hAChE enzyme in vitro and in vivo tests. (C) 2021 Elsevier B.V. All rights reserved.
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| 3. |
- Ebrahimi, Fatemeh, et al.
(författare)
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Ethanol production from bread residues
- 2008
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Ingår i: Biomass and Bioenergy. - : Elsevier BV. - 0961-9534 .- 1873-2909. ; 32:4, s. 333-337
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Tidskriftsartikel (refereegranskat)
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| 4. |
- Griswold, Max G., et al.
(författare)
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Alcohol use and burden for 195 countries and territories, 1990-2016 : a systematic analysis for the Global Burden of Disease Study 2016
- 2018
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Ingår i: The Lancet. - : Elsevier. - 0140-6736 .- 1474-547X. ; 392:10152, s. 1015-1035
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Tidskriftsartikel (refereegranskat)abstract
- Background: Alcohol use is a leading risk factor for death and disability, but its overall association with health remains complex given the possible protective effects of moderate alcohol consumption on some conditions. With our comprehensive approach to health accounting within the Global Burden of Diseases, Injuries, and Risk Factors Study 2016, we generated improved estimates of alcohol use and alcohol-attributable deaths and disability-adjusted life-years (DALYs) for 195 locations from 1990 to 2016, for both sexes and for 5-year age groups between the ages of 15 years and 95 years and older.Methods: Using 694 data sources of individual and population-level alcohol consumption, along with 592 prospective and retrospective studies on the risk of alcohol use, we produced estimates of the prevalence of current drinking, abstention, the distribution of alcohol consumption among current drinkers in standard drinks daily (defined as 10 g of pure ethyl alcohol), and alcohol-attributable deaths and DALYs. We made several methodological improvements compared with previous estimates: first, we adjusted alcohol sales estimates to take into account tourist and unrecorded consumption; second, we did a new meta-analysis of relative risks for 23 health outcomes associated with alcohol use; and third, we developed a new method to quantify the level of alcohol consumption that minimises the overall risk to individual health.Findings: Globally, alcohol use was the seventh leading risk factor for both deaths and DALYs in 2016, accounting for 2.2% (95% uncertainty interval [UI] 1.5-3.0) of age-standardised female deaths and 6.8% (5.8-8.0) of age-standardised male deaths. Among the population aged 15-49 years, alcohol use was the leading risk factor globally in 2016, with 3.8% (95% UI 3.2-4-3) of female deaths and 12.2% (10.8-13-6) of male deaths attributable to alcohol use. For the population aged 15-49 years, female attributable DALYs were 2.3% (95% UI 2.0-2.6) and male attributable DALYs were 8.9% (7.8-9.9). The three leading causes of attributable deaths in this age group were tuberculosis (1.4% [95% UI 1. 0-1. 7] of total deaths), road injuries (1.2% [0.7-1.9]), and self-harm (1.1% [0.6-1.5]). For populations aged 50 years and older, cancers accounted for a large proportion of total alcohol-attributable deaths in 2016, constituting 27.1% (95% UI 21.2-33.3) of total alcohol-attributable female deaths and 18.9% (15.3-22.6) of male deaths. The level of alcohol consumption that minimised harm across health outcomes was zero (95% UI 0.0-0.8) standard drinks per week.Interpretation: Alcohol use is a leading risk factor for global disease burden and causes substantial health loss. We found that the risk of all-cause mortality, and of cancers specifically, rises with increasing levels of consumption, and the level of consumption that minimises health loss is zero. These results suggest that alcohol control policies might need to be revised worldwide, refocusing on efforts to lower overall population-level consumption.
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| 5. |
- Nabavinejad, Seyed Morteza, et al.
(författare)
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BatchSizer : Power-Performance Trade-off for DNN Inference
- 2021
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Ingår i: 2021 26TH ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE (ASP-DAC). - New York, NY, USA : IEEE. ; , s. 819-824
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Konferensbidrag (refereegranskat)abstract
- GPU accelerators can deliver significant improvement for DNN processing; however, their performance is limited by internal and external parameters. A well-known parameter that restricts the performance of various computing platforms in real-world setups, including GPU accelerators, is the power cap imposed usually by an external power controller. A common approach to meet the power cap constraint is using the Dynamic Voltage Frequency Scaling (DVFS) technique. However, the functionally of this technique is limited and platform-dependent. To improve the performance of DNN inference on GPU accelerators, we propose a new control knob, which is the size of input batches fed to the GPU accelerator in DNN inference applications. After evaluating the impact of this control knob on power consumption and performance of GPU accelerators and DNN inference applications, we introduce the design and implementation of a fast and lightweight runtime system, called BatchSizer. This runtime system leverages the new control knob for managing the power consumption of GPU accelerators in the presence of the power cap. Conducting several experiments using a modern GPU and several DNN models and input datasets, we show that our BatchSizer can significantly surpass the conventional DVFS technique regarding performance (up to 29%), while successfully meeting the power cap.
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| 6. |
- Nabavinejad, Seyed Morteza, et al.
(författare)
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Coordinated Batching and DVFS for DNN Inference on GPU Accelerators
- 2022
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Ingår i: IEEE Transactions on Parallel and Distributed Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 1045-9219 .- 1558-2183. ; 33:10, s. 2496-2508
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Tidskriftsartikel (refereegranskat)abstract
- Employing hardware accelerators to improve the performance and energy-efficiency of DNN applications is on the rise. One challenge of using hardware accelerators, including the GPU-based ones, is that their performance is limited by internal and external factors, such as power caps. A common approach to meet the power cap constraint is using the Dynamic Voltage Frequency Scaling (DVFS) technique. However, the functionally of this technique is limited and platform-dependent. To tackle this challenge, we propose a new control knob, which is the size of input batches fed to the GPU accelerator in DNN inference applications. We first evaluate the impact of batch size on power consumption and performance of DNN inference. Then, we introduce the design and implementation of a fast and lightweight runtime system, called BatchDVFS. Dynamic batching is implemented in BatchDVFS to adaptively change the batch size, and hence, trade-off throughput with power consumption. It employs an approach based on binary search to find the proper batch size within a short period of time. Combining dynamic batching with the DVFS technique, BatchDVFS can control the power consumption in wider ranges, and hence, yield higher throughput in the presence of power caps. To find near-optimal solution for long-running jobs that can afford a relatively significant profiling overhead, compared with BatchDVFS overhead, we also design an approach, called BOBD, that employs Bayesian Optimization to wisely explore the vast state space resulted by combination of the batch size and DVFS solutions. Conducting several experiments using a modern GPU and several DNN models and input datasets, we show that our BatchDVFS can significantly surpass the techniques solely based on DVFS or batching, regarding throughput (up to 11.2x and 2.2x, respectively), while successfully meeting the power cap.
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| 7. |
- Nakhaei, Niknaz, et al.
(författare)
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A solution technique to cascading link failure prediction
- 2022
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Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 258
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Tidskriftsartikel (refereegranskat)abstract
- The study of complex networks is a new powerful tool that can provide a profitable skeleton to better elucidate technology-related phenomena and interactions between components of real-world networks However, it is not easy to predict the communal behavior of such systems from their elements and on the other hand, the failure of one or few elements can trigger the failure of other elements throughout the network, resulting in network breakdown and catastrophic events at large scales. Therefore, developing predictive mathematical techniques to examine complex networks is one of the biggest challenges of the present time. Knowing that link failure prediction is less studied in the OR literature, the present study articulates a method to predict link failures in complex networks, which is primarily based on Bayesian Belief Networks (BBN) and TOPSIS. The method aims to predict failures based on the affective factors of failures in networks. To this end, effective factors of failures are first detected, and then the graph of the relationship of factors along with their weight is determined. After all, the method provides the prediction for future damaged components. The functionality of the method is validated by an extensive computational analysis employing simulation in scale-free, random, and actual international aviation networks and its performance is compared with other benchmark algorithms. The results and sensitivity analysis experiments arrive at prominent managerial insights and efficacious implications and show that our method can generate high-quality solutions in many networks.
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| 8. |
- Sadou, Isma-Ilou, et al.
(författare)
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Inference Time Reduction of Deep Neural Networks on Embedded Devices : A Case Study
- 2022
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Ingår i: 2022 25Th Euromicro Conference On Digital System Design (DSD). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 205-213
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Konferensbidrag (refereegranskat)abstract
- From object detection to semantic segmentation, deep learning has achieved many groundbreaking results in recent years. However, due to the increasing complexity, the execution of neural networks on embedded platforms is greatly hindered. This has motivated the development of several neural network minimisation techniques, amongst which pruning has gained a lot of focus. In this work, we perform a case study on a series of methods with the goal of finding a small model that could run fast on embedded devices. First, we suggest a simple, but effective, ranking criterion for filter pruning called Mean Weight. Then, we combine this new criterion with a threshold-aware layer-sensitive filter pruning method, called T-sensitive pruning, to gain high accuracy. Further, the pruning algorithm follows a structured filter pruning approach that removes all selected filters and their dependencies from the DNN model, leading to less computations, and thus low inference time in lower-end CPUs. To validate the effectiveness of the proposed method, we perform experiments on three different datasets (with 3, 101, and 1000 classes) and two different deep neural networks (i.e., SICK-Net and MobileNet V1). We have obtained speedups of up to 13x on lower-end CPUs (Armv8) with less than 1% drop in accuracy. This satisfies the goal of transferring deep neural networks to embedded hardware while attaining a good trade-off between inference time and accuracy.
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| 9. |
- Stanaway, Jeffrey D., et al.
(författare)
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Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017
- 2018
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Ingår i: The Lancet. - 1474-547X .- 0140-6736. ; 392:10159, s. 1923-1994
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Tidskriftsartikel (refereegranskat)abstract
- Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk-outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk-outcome pairs, and new data on risk exposure levels and risk- outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017.
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