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Cost-aware cloud workflow scheduling using DRL and simulated annealing
Get PDFCloud workloads are highly dynamic and complex, making task scheduling in cloud computing a challenging problem. While
several scheduling algorithms have been proposed in recent years, they are mainly designed to handle batch tasks and not well-suited for real-time workloads. To address this issue, researchers have started exploring the use of Deep Reinforcement Learning (DRL). However, the existing models are limited in handling independent tasks and cannot process workflows, which are prevalent in cloud computing and consist of related subtasks. In this paper, we propose SA-DQN, a scheduling approach specifically designed for real-time cloud workflows. Our approach seamlessly integrates the Simulated Annealing (SA) algorithm and Deep Q-Network (DQN) algorithm. The SA algorithm is employed to determine an optimal execution order of subtasks in a cloud server, serving as a crucial feature of the task for the neural network to learn. We provide a detailed design of our approach and show that SA-DQN outperforms existing algorithms in terms of handling real-time cloud workflows through experimental results
Hydro-pedotransfer functions: a roadmap for future development
Get PDFHydro-pedotransfer functions (PTFs) relate easy-to-measure and readily available soil information to soil hydraulic properties (SHPs) for applications in a wide range of process-based and empirical models, thereby enabling the assessment of soil hydraulic effects on hydrological, biogeochemical, and ecological processes. At least more than four decades of research have been invested to derive such relationships. However, while models, methods, data storage capacity, and computational efficiency have advanced, there are fundamental concerns related to the scope and adequacy of current PTFs, particularly when applied to parameterize models used at the field scale and beyond. Most of the PTF development process has focused on refining and advancing the regression methods, while fundamental aspects have remained largely unconsidered. Most soil systems are not represented in in PTFs, which have been built mostly for agricultural soils in temperate climates. Thus, existing
PTFs largely ignore how parent material, vegetation, land use, and climate affect processes that shape SHPs. The PTFs used to parameterise the Richards-Richardson equation (RRE) are mostly limited to predicting parameters of the van Genuchten Mualem (VGM) soil hydraulic functions, despite sufficient evidence demonstrating their shortcomings. Another fundamental issue relates to the diverging scales of derivation and application, whereby PTFs are derived based on laboratory measurements while being often applied at field to regional scales. Scaling, modulation, and constraining strategies exist to alleviate some of
these shortcomings in the mismatch between scales. These aspects are addressed here in a joint effort by the members of the International Soil Modelling Consortium (ISMC) Pedotransfer Functions Working Group with the aim to systematise PTF research and provide a roadmap guiding both PTF development and use. We close with a ten-point catalogue for funders and researchers to guide review processes and research
Artificial intelligence for climate prediction of extremes: state of the art, challenges, and future perspectives
Get PDFExtreme events such as heat waves and cold spells, droughts, heavy rain, and storms are particularly challenging to predict accurately due to their rarity and chaotic nature, and because of model limitations. However, recent studies have shown that there might be systemic predictability that is not being leveraged, whose exploitation could meet the need for reliable predictions of aggregated extreme weather measures on timescales from weeks to decades ahead. Recently, numerous studies have been devoted to the use of artificial intelligence (AI) to study predictability and make climate predictions. AI techniques have shown great potential to improve the prediction of extreme events and uncover their links to large‐scale and local drivers. Machine and deep learning have been explored to enhance prediction, while causal discovery and explainable AI have been tested to improve our understanding of the processes underlying predictability. Hybrid predictions combining AI, which can reveal unknown spatiotemporal connections from data, with climate models that provide the theoretical foundation and interpretability of the physical world, have shown that improving prediction skills of extremes on climate‐relevant timescales is possible. However, numerous challenges persist in various aspects, including data curation, model uncertainty, generalizability, reproducibility of methods, and workflows. This review aims at overviewing achievements and challenges in the use of AI techniques to improve the prediction of extremes at the subseasonal to decadal timescale. A few best practices are identified to increase trust in these novel techniques, and future perspectives are envisaged for further scientific development
Entrenched intransigence or emancipated enlightenment? Building or destroying something within the spotlight of conflict
Get PDFBuildings are places of safety and conflict. When shells cracked the horizon and my world in my country of Syria, I sought cultural heritage buildings (CHBs) for quietness and security. I am now understanding that these very buildings are caldrons of entrenched memories, and negotiation spaces of future settlements or, perhaps, of unease. The buildings are not limited to cultural heritage - rather they expand to cultural futures. They have a distinctive agency that emanates from their deep context and history. This paper revisits the literature on CHBs from its cosy moorings of preservation and conservation. We offer a warzone perspective. An ambiguous hinterland where CHBs become the catalyst of simmering grievance that implicate and dictate future conflict or reconciliation. Through a prism of unfolding autoethnography of the lead researcher's experience of the recent Syrian war, the literature review traces boundaries, asserting the need to explore the social and personal questions beckoning with CHBs, and the trauma of the sudden shift from more entrenched rules in stable times towards unprecedented rules (chaos?) conditioned by war. This paper will contribute to an inside-out perspective to the meanings of CHBs in warzones and gives possible future direction
Microbially mediated phenolic catabolites exert differential genoprotective activities in normal and adenocarcinoma cell lines
Get PDFAge-associated decline of nuclear factor erythroid 2-related factor 2 (Nrf2) activity and DNA repair efficiency leads to the accumulation of DNA damage and increased risk of cancer. Understanding the mechanisms behind increased levels of damaged DNA is crucial for developing interventions to mitigate age-related cancer risk. Associated with various health benefits, (poly)phenols and their microbially mediated phenolic catabolites represent a potential means to reduce DNA damage. Four colonic-microbiota-derived phenolic catabolites were investigated for their ability to reduce H2O2-induced oxidative DNA damage and modulate the Nrf2-Antixoidant Response Element (ARE) pathway, in normal (CCD 841 CoN) and adenocarcinoma (HT29) colonocyte cell lines. Each catabolite demonstrated significant (p < .001) genoprotective activity and modulation of key genes in the Nrf2-ARE pathway. Overall, the colon-derived phenolic metabolites, when assessed at physiologically relevant concentrations, reduced DNA damage in both normal and adenocarcinoma colonic cells in response to oxidative challenge, mediated in part via upregulation of the Nrf2-ARE pathway
Modelling the continuum of macrophage phenotypes and their role in inflammation
Get PDFMacrophages are a type of white blood cell that play a significant role in determining the inflammatory response associated with a wide range of medical conditions. They are highly plastic, having the capacity to adopt numerous polarisation states or ‘phenotypes’ with disparate pro- or anti-inflammatory roles. Many previous studies divide macrophages into two categorisations: M1 macrophages are largely pro-inflammatory in nature, while M2 macrophages are largely restorative. However, there is a growing body of evidence that the M1 and M2 classifications represent the extremes of a much broader spectrum of phenotypes, and that intermediate phenotypes can play important roles in the progression or treatment of many medical conditions. In this article, we present a model of macrophage dynamics that includes a continuous description of phenotype, and hence incorporates intermediate phenotype configurations. We describe macrophage phenotype switching via nonlinear convective flux terms that scale with background levels of generic pro- and anti-inflammatory mediators. Through numerical simulation and bifurcation analysis, we unravel the model’s resulting dynamics, paying close attention to the system’s multistability and the extent to which key macrophage–mediator interactions provide bifurcations that act as switches between chronic states and restoration of health. We show that interactions that promote M1-like phenotypes generally result in a greater array of stable chronic states, while interactions that promote M2-like phenotypes can promote restoration of health. Additionally, our model admits oscillatory solutions reminiscent of relapsing–remitting conditions, with macrophages being largely polarised toward anti-inflammatory activity during remission, but with intermediate phenotypes playing a role in inflammatory flare-ups. We conclude by reflecting on our observations in the context of the ongoing pursuance of novel therapeutic interventions
The impact of preceding convection on the development of Medicane Ianos and the sensitivity to sea surface temperature
Get PDFMedicane Ianos in September 2020 was one of the strongest medicanes observed in the last 25 years. It was, like other medicanes, a very intense cyclone evolving from a baroclinic mid-latitude low into a tropical-like cyclone with an axisymmetric warm core. The dynamical elements necessary to improve the predictability of Ianos are explored with the use of simulations with the Met Office Unified Model (MetUM) at 2.2 km grid spacing for five different initialisation times, from 4 to 2 d before Ianos's landfall. Simulations are also performed with the sea surface temperature (SST) uniformly increased and decreased by 2 K from analysis to explore the impact of enhanced and reduced sea surface fluxes on Ianos's evolution. All the simulations with +2 K SST are able to simulate Medicane Ianos, albeit too intensely. The simulations with control SST initialised at the two earliest times fail to capture intense preceding precipitation events at the right locations and the subsequent development of Ianos. Amongst the simulations with −2 K SST, only the one initialised at the latest time develops the medicane.
Links between sea surface fluxes and upper-level baroclinic processes are investigated. We find three elements that are important for Ianos's development. First, an area of low-valued potential vorticity (PV), termed a “low-PV bubble”, formed within a trough above where Ianos developed; diabatic heating associated with a preceding precipitation event triggered a balanced divergent flow in the upper levels, which contributed to the creation and maintenance of this low-PV bubble as shown by results from a semi-geostrophic inversion tool. Second, a quasi-geostrophic ascent was forced by middle and upper levels during Ianos's cyclogenesis. It is partially associated with the geostrophic vorticity advection, which is enhanced by the growth and advection of the low-PV bubble. Third, diabatic heating dominated by deep convection formed a vertical PV tower during Ianos's intensification and continued to produce diabatically induced divergent outflow aloft, thus sustaining Ianos’s development. Simulations missing any of these three elements do not develop Medicane Ianos.
Our results imply the novel finding that preceding convection was essential for the subsequent development of Ianos, highlighting the importance of the interactions between near-surface small-scale diabatic processes and the upper-level quasi-geostrophic flow. A warmer SST strengthens the processes and thus enables Ianos to be predicted in simulations initiated at the earlier times
Structure and physical properties of mixed-metal chalcogenides for energy applications
No full textThermoelectricity offers a promising solution to help address the ongoing energy crisis by
utilizing waste heat and reducing the reliance on conventional energy sources thereby
contributing to a more sustainable and environmentally friendly energy future. A thermoelectric
device allows for the direct conversion of thermal energy to electrical energy or vice versa
through several principles. The structural and thermoelectric properties have been investigated
for three families of mixed-metal chalcogenides materials. Chemical substitution approaches
have been used on all the prepared materials as a method to enhance the thermoelectric
performance.
Focusing on Cu2BGeSe4 (B = Fe, Mn and Co), the influence of magnetic ions on crystal
structure and thermoelectric properties was explored. Different crystal structures are adopted
based on the magnetic ion present, with Cu2FeGeSe4 and Cu2MnGeSe4 exhibiting structural
phase transitions at ~ 500 K affecting their electrical properties. The phase Cu2CoGeSe4, which
was characterised by the increased tetragonal distortion parameter, achieved a maximum figure
of merit ZT of 0.52 at 875 K. Magnetic studies uncovered antiferromagnetic order in
Cu2MnGeSe4. Neutron diffraction data revealed magnetic scattering in Cu2MnGeSe4 phase,
while Cu2FeGeSe4 showed no evidence of long-range magnetic ordering which indicates a spin-glass transition. The magnetic unit-cell of Cu2MnGeSe4 is doubled in the a and c directions and
is defined by the propagation vector k = [1/2, 0, 1/2] with an ordered magnetic moment of μ =
3.950(2) μB at 5 K. The room-temperature analysis of neutron diffraction data for Cu2FeGeSe4
shows an antisite cation disorder at the 4d and 2a sites.
Investigations of the effect of magnetic ions on the thermoelectric properties were extended to
the mixed derivatives, Cu2Fe1-xMxGeSe4 (M=Mn and Co) (0 ≤ x ≤ 1), in which improvements
in figure of merit and the average figure of merit were observed, with Cu2Fe0.925Co0.075GeSe4
achieving the maximum ZT = 0.53 at 800 K. Tunning the carrier concentration through electron
doping was investigated in the series Cu2FeGeSe4-x (0 ≤ x ≤ 0.3). Reducing the Se content
increases the figure of merit reaching ZT = 0.47 at 875 K for Cu2FeGeSe3.90 in comparison with
the end-member phase.
The new kiddcreekite-type materials have been investigated as a potential TE materials. The
materials are of interest for their complex crystal structure, large unit-cell and the presence of
heavy atoms. X-ray powder diffraction coupled with Rietveld refinements confirmed the cubic
structure with the space group F4̅3m and lattice parameters a = 10.8328(1) Å and a = 11.2781(2)
Å for Cu6SnWS8 and Cu6SnWSe8 respectively. Cu6SnWS8 outperformed Cu6SnWSe8 in the
figure of merit due to its lower electrical resistivity and enhanced Seebeck coefficient, attributed
Alaa Aldowiesh to point-defects and off-stoichiometry that altered the electronic band structure. The maximum
figure of merit reached ZT = 2.3 × 10-4 at 575 K for Cu6SnWSe8 and ZT = 0.021 at 675 K for
Cu6SnWS8. Substituting components at different sites within the kiddcreekite materials (24f ,
4c, 4a and 16e) through electron, hole, and isovalent substitution shows limited effectiveness in
improving ZT, as the substitution negatively impacted the electrical transport properties.
Work on p-type two-dimensional materials demonstrated that Cr2-xInxGe2Te6 (x = 0, 1, 2) based
compounds are good candidates for thermoelectric applications. The phases Cr2Ge2Te6 and
In2Ge2Te6 crystallise in the trigonal space group R3̅, whereas the phase CrInGe2Te6 crystallises
in the trigonal space group P3̅1c. Replacing one Cr3+ with In3+ increased the figure of merit
reaching ZT⊥ = 0.18 at 730 K in CrInGe2Te6. However, the power factor in the undoped material,
CrInGe2Te6, is lower than that of conventional thermoelectric materials. This indicates that
further enhancement of the electrical properties can be achieved through chemical substitution.
The thermoelectric performance is greatly enhanced in the two series Cr1-xMnxInGe2Te6 and
CrIn1-xMnxGe2Te6 (0 ≤ x ≤ 0.1). The substitution reduced the electrical resistivity, ρ, and this
reduction offset the insignificant increase in the thermal conductivity. Hole doping enhanced
the thermoelectric performance by up to 67% and 60% in Cr0.92Mn0.08InGe2Te6 and
CrIn0.92Mn0.08Ge2Te6 ,respectively, reaching ZT⊥ = 0.52 and ZT⊥ = 0.42 at 730 K
Tropical intraseasonal oscillations as key driver and source of predictability for the 2022 Pakistan record-breaking rainfall event
Get PDFIn August 2022, Pakistan experienced unprecedented monsoon rains, leading to devastating floods and landslides affecting millions. While previous research has mainly focused on the contributions of seasonal and synoptic anomalies, this study elucidates the dominant influences of tropical and extratropical intraseasonal oscillations on both the occurrence and subseasonal prediction of this extreme rainfall event. Our scale-decomposed moisture budget analysis revealed that intense rainfall in Pakistan was triggered and sustained by enhanced vertical moisture transport anomalies, primarily driven by interactions between intraseasonal circulation anomalies and the prevailing background moisture field when tropical and mid-latitude systems coincided over Pakistan. Evaluation of subseasonal-to-seasonal prediction models further highlighted the critical role of tropical intraseasonal modes in causing this extreme rainfall event in Pakistan. Models that accurately predicted northward-propagating intraseasonal convection with a forecast lead time of 8-22 days demonstrated good skill in predicting the extreme event over Pakistan
