Introducing algorithm portfolios to a class of vehicle routing and scheduling problem

The paper presents a comprehensive foundation and implementation of Algorithm Portfolios to solve Theater Distribution Vehicle Routing and Scheduling Problems (TDVRSP). In order to evaluate the performance of proposed approach, it has been applied to varying dimensions of theater distribution problem. In particular, eight random search metaheuristics embedded in four processors, packed to form different portfolios. Four basic algorithms- Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS) and Artificial Immune System (AIS), as well as their group theoretic counterparts have been utilized. The proposed approach also takes care of platform dependence and helps evolving a robust solution pack. The portfolio concept is shown to be computationally advantageous and qualitatively competitive over the benchmark set of problems. The paper does not only provide modeling to TDVRSP, but also aids in developing a generic solution framework for other problems of its kind

Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations

We employed a global high-resolution inverse model to optimize the CH4 emission using Greenhouse gas Observing Satellite (GOSAT) and surface observation data for a period from 2011–2017 for the two main source categories of anthropogenic and natural emissions. We used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission and scaled them by country to match the national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using the Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimated a global total anthropogenic and natural methane emissions of 340.9 Tg CH4 yr−1 and 232.5 Tg CH4 yr−1, respectively. Country-scale analysis of the estimated anthropogenic emissions showed that all the top-emitting countries showed differences with their respective inventories to be within the uncertainty range of the inventories, confirming that the posterior anthropogenic emissions did not deviate from nationally reported values. Large countries, such as China, Russia, and the United States, had the mean estimated emission of 45.7 ± 8.6, 31.9 ± 7.8, and 29.8 ± 7.8 Tg CH4 yr−1, respectively. For natural wetland emissions, we estimated large emissions for Brazil (39.8 ± 12.4 Tg CH4 yr−1), the United States (25.9 ± 8.3 Tg CH4 yr−1), Russia (13.2 ± 9.3 Tg CH4 yr−1), India (12.3 ± 6.4 Tg CH4 yr−1), and Canada (12.2 ± 5.1 Tg CH4 yr−1). In both emission categories, the major emitting countries all had the model corrections to emissions within the uncertainty range of inventories. The advantages of the approach used in this study were: (1) use of high-resolution transport, useful for simulations near emission hotspots, (2) prior anthropogenic emissions adjusted to the UNFCCC reports, (3) combining surface and satellite observations, which improves the estimation of both natural and anthropogenic methane emissions over spatial scale of countries

Simulating CH_4 and CO_2 over South and East Asia using the zoomed chemistry transport model LMDz-INCA

The increasing availability of atmospheric measurements of greenhouse gases (GHGs) from surface stations can improve the retrieval of their fluxes at higher spatial and temporal resolutions by inversions, provided that transport models are able to properly represent the variability of concentrations observed at different stations. South and East Asia (SEA; the study area in this paper including the regions of South Asia and East Asia) is a region with large and very uncertain emissions of carbon dioxide (CO_2) and methane (CH_4), the most potent anthropogenic GHGs. Monitoring networks have expanded greatly during the past decade in this region, which should contribute to reducing uncertainties in estimates of regional GHG budgets. In this study, we simulate concentrations of CH_4 and CO_2 using zoomed versions (abbreviated as ZAs) of the global chemistry transport model LMDz-INCA, which have fine horizontal resolutions of  ∼ 0.66° in longitude and  ∼ 0.51° in latitude over SEA and coarser resolutions elsewhere. The concentrations of CH_4 and CO_2 simulated from ZAs are compared to those from the same model but with standard model grids of 2.50° in longitude and 1.27° in latitude (abbreviated as STs), both prescribed with the same natural and anthropogenic fluxes. Model performance is evaluated for each model version at multi-annual, seasonal, synoptic and diurnal scales, against a unique observation dataset including 39 global and regional stations over SEA and around the world. Results show that ZAs improve the overall representation of CH_4 annual gradients between stations in SEA, with reduction of RMSE by 16–20% compared to STs. The model improvement mainly results from reduction in representation error at finer horizontal resolutions and thus better characterization of the CH_4 concentration gradients related to scattered distributed emission sources. However, the performance of ZAs at a specific station as compared to STs is more sensitive to errors in meteorological forcings and surface fluxes, especially when short-term variabilities or stations close to source regions are examined. This highlights the importance of accurate a priori CH_4 surface fluxes in high-resolution transport modeling and inverse studies, particularly regarding locations and magnitudes of emission hotspots. Model performance for CO_2 suggests that the CO_2 surface fluxes have not been prescribed with sufficient accuracy and resolution, especially the spatiotemporally varying carbon exchange between land surface and atmosphere. In addition, the representation of the CH_4 and CO_2 short-term variabilities is also limited by model's ability to simulate boundary layer mixing and mesoscale transport in complex terrains, emphasizing the need to improve sub-grid physical parameterizations in addition to refinement of model resolutions

Simulating CH_4 and CO_2 over South and East Asia using the zoomed chemistry transport model LMDz-INCA

The increasing availability of atmospheric measurements of greenhouse gases (GHGs) from surface stations can improve the retrieval of their fluxes at higher spatial and temporal resolutions by inversions, provided that transport models are able to properly represent the variability of concentrations observed at different stations. South and East Asia (SEA; the study area in this paper including the regions of South Asia and East Asia) is a region with large and very uncertain emissions of carbon dioxide (CO_2) and methane (CH_4), the most potent anthropogenic GHGs. Monitoring networks have expanded greatly during the past decade in this region, which should contribute to reducing uncertainties in estimates of regional GHG budgets. In this study, we simulate concentrations of CH_4 and CO_2 using zoomed versions (abbreviated as ZAs) of the global chemistry transport model LMDz-INCA, which have fine horizontal resolutions of  ∼ 0.66° in longitude and  ∼ 0.51° in latitude over SEA and coarser resolutions elsewhere. The concentrations of CH_4 and CO_2 simulated from ZAs are compared to those from the same model but with standard model grids of 2.50° in longitude and 1.27° in latitude (abbreviated as STs), both prescribed with the same natural and anthropogenic fluxes. Model performance is evaluated for each model version at multi-annual, seasonal, synoptic and diurnal scales, against a unique observation dataset including 39 global and regional stations over SEA and around the world. Results show that ZAs improve the overall representation of CH_4 annual gradients between stations in SEA, with reduction of RMSE by 16–20% compared to STs. The model improvement mainly results from reduction in representation error at finer horizontal resolutions and thus better characterization of the CH_4 concentration gradients related to scattered distributed emission sources. However, the performance of ZAs at a specific station as compared to STs is more sensitive to errors in meteorological forcings and surface fluxes, especially when short-term variabilities or stations close to source regions are examined. This highlights the importance of accurate a priori CH_4 surface fluxes in high-resolution transport modeling and inverse studies, particularly regarding locations and magnitudes of emission hotspots. Model performance for CO_2 suggests that the CO_2 surface fluxes have not been prescribed with sufficient accuracy and resolution, especially the spatiotemporally varying carbon exchange between land surface and atmosphere. In addition, the representation of the CH_4 and CO_2 short-term variabilities is also limited by model's ability to simulate boundary layer mixing and mesoscale transport in complex terrains, emphasizing the need to improve sub-grid physical parameterizations in addition to refinement of model resolutions

Climate Change and COP26: Are Digital Technologies and Information Management Part of the Problem or the Solution? An Editorial Reflection and Call to Action

The UN COP26 2021 conference on climate change offers the chance for world leaders to take action and make urgent and meaningful commitments to reducing emissions and limit global temperatures to 1.5 °C above pre-industrial levels by 2050. Whilst the political aspects and subsequent ramifications of these fundamental and critical decisions cannot be underestimated, there exists a technical perspective where digital and IS technology has a role to play in the monitoring of potential solutions, but also an integral element of climate change solutions. We explore these aspects in this editorial article, offering a comprehensive opinion based insight to a multitude of diverse viewpoints that look at the many challenges through a technology lens. It is widely recognized that technology in all its forms, is an important and integral element of the solution, but industry and wider society also view technology as being part of the problem. Increasingly, researchers are referencing the importance of responsible digitalization to eliminate the significant levels of e-waste. The reality is that technology is an integral component of the global efforts to get to net zero, however, its adoption requires pragmatic tradeoffs as we transition from current behaviors to a more climate friendly society

Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action

The UN COP26 2021 conference on climate change offers the chance for world leaders to take action and make urgent and meaningful commitments to reducing emissions and limit global temperatures to 1.5 °C above pre-industrial levels by 2050. Whilst the political aspects and subsequent ramifications of these fundamental and critical decisions cannot be underestimated, there exists a technical perspective where digital and IS technology has a role to play in the monitoring of potential solutions, but also an integral element of climate change solutions. We explore these aspects in this editorial article, offering a comprehensive opinion based insight to a multitude of diverse viewpoints that look at the many challenges through a technology lens. It is widely recognized that technology in all its forms, is an important and integral element of the solution, but industry and wider society also view technology as being part of the problem. Increasingly, researchers are referencing the importance of responsible digitalization to eliminate the significant levels of e-waste. The reality is that technology is an integral component of the global efforts to get to net zero, however, its adoption requires pragmatic tradeoffs as we transition from current behaviors to a more climate friendly society.</p

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts