Evolutionary algorithms (EAs) are a kind of nature-inspired general-purpose optimization algorithm, and have shown empirically good performance in solving various real-word optimization problems. ...During the past two decades, promising results on the running time analysis (one essential theoretical aspect) of EAs have been obtained, while most of them focused on isolated combinatorial optimization problems, which do not reflect the general-purpose nature of EAs. To provide a general theoretical explanation of the behavior of EAs, it is desirable to study their performance on general classes of combinatorial optimization problems. To the best of our knowledge, the only result towards this direction is the provably good approximation guarantees of EAs for the problem class of maximizing monotone submodular functions with matroid constraints. The aim of this work is to contribute to this line of research. Considering that many combinatorial optimization problems involve non-monotone or non-submodular objective functions, we study the general problem classes, maximizing submodular functions with/without a size constraint and maximizing monotone approximately submodular functions with a size constraint. We prove that a simple multi-objective EA called GSEMO-C can generally achieve good approximation guarantees in polynomial expected running time.
•Electrochemical sensor for heavy metal quantification in water samples without pretreatments.•The local pH can be adjusted by tailoring the applied potential to the protonator electrode.•Very low ...LOD for Cu and Hg were demonstrated with extremely high sensitivity.•This technique allowed detection of these metals in river water, without sample pretreatment.•In-situ pH control can allow heavy metal in real time and in situ analysis by unskilled personnel in remote settings.
The performance of electrochemical sensors using an in situ pH control technique for detection of mercury and copper in neutral solutions is described herein. Sensors are comprised of two distinct parallel gold interdigitated microband electrodes each of which may be polarised separately. Biasing one interdigitated “protonator” electrode sufficiently positive to begin water electrolysis, resulted in the production of H+ ions, which, consequently droped the interfacial pH at the other second interdigitated “sensing” electrode. This decrease in pH permitted the electrodeposition (and consequent stripping) of metals at a sensing electrode without the need to acidify the whole test solution. In this work, the local pH could be adjusted in the acidic pH range in a stable and reproducible way just by tailoring the polarization of the protonator electrode. Using this approach, a linear range for copper 5 to 100 ppb and for mercury 1 to 75 ppb were obtained. The sensors also have an extremely high sensitivity for the metals. The in-situ pH control, coupled with electrochemical stripping, allowed metal detection in a complex water matrix, e.g., river water without the need for sample pre-treatment. The electrochemical results were confirmed by comparison to those obtained using inductively coupled plasma – optical emission spectroscopy. A very good agreement was observed between both sets of results. The electrode reproducibility was high (RSD < 10%) and the metals could be co-detected simultaneously. Thus, this work shows a fast and easy approach for in-situ pH control for multi metal detection using solid state sensors.
This study investigates the prevalence of violent language on incels.is . It evaluates GPT models (GPT-3.5 and GPT-4) for content analysis in social sciences, focusing on the impact of varying ...prompts and batch sizes on coding quality for the detection of violent speech. We scraped over 6.9 M posts from incels.is and categorized a random sample into non-violent, explicitly violent, and implicitly violent content. Two human coders annotated 3, 028 posts, which we used to tune and evaluate GPT-3.5 and GPT-4 models across different prompts and batch sizes regarding coding reliability. The best-performing GPT-4 model annotated an additional 45, 611 posts for further analysis. We find that 21.91% of the posts on the forum contain some form of violent language. Within the overall forum, 18.12% of posts include explicit violence, while 3.79% feature implicit violence. Our results show a significant rise in violent speech on incels.is , both at the community and individual level. This trend is particularly pronounced among users with an active posting behavior that lasts for several hours up to one month. While the use of targeted violent language decreases, general violent language increases. Additionally, mentions of self-harm decline, especially for users who have been active on the site for over 2.5 years. We find substantial agreement between both human coders (κ = 0.65), while the best GPT-4 model yields good agreement with both human coders (κ = 0.54 for Human A and κ = 0.62 for Human B). Overall, this research offers effective ways to pinpoint violent language on a large scale, helping with content moderation and facilitating further research into causal mechanisms and potential mitigations of violent expression and online radicalization in communities like incels.is .
We propose the first method that determines the exact worst-case execution time (WCET) for implicit linear model predictive control (MPC). Such WCET bounds are imperative when MPC is used in real ...time to control safety-critical systems. The proposed method applies when the quadratic programming solver in the MPC controller belongs to a family of well-established active-set solvers. For such solvers, we leverage a previously proposed complexity certification framework to generate a finite set of "archetypal" optimization problems; we prove that these archetypal problems form an execution-time equivalent cover of all possible problems; that is, that they capture the execution time for solving any possible optimization problem that can be encountered online. Hence, by solving just these archetypal problems on the hardware on which the MPC is to be deployed, and by recording the execution times, we obtain the exact WCET. In addition to providing formal proofs of the methods efficacy, we validate the method on an MPC example where an inverted pendulum on a cart is stabilized. The experiments highlight the following advantages compared with classical WCET methods: (i) in contrast to classical static methods, our method gives the exact WCET; (ii) in contrast to classical measurement-based methods, our method guarantees a correct WCET estimate and requires fewer measurements on the hardware.
This paper studies the workload offloading problem for fog computing networks in which a set of fog nodes can offload part or all the workload originally targeted to the cloud data centers to further ...improve the quality-of-experience (QoE) of users. We investigate two performance metrics for fog computing networks: users' QoE and fog nodes' power efficiency. We observe a fundamental tradeoff between these two metrics for fog computing networks. We then consider cooperative fog computing networks in which multiple fog nodes can help each other to jointly offload workload from cloud data centers. We propose a novel cooperation strategy referred to as offload forwarding, in which each fog node, instead of always relying on cloud data centers to process its unprocessed workload, can also forward part or all of its unprocessed workload to its neighboring fog nodes to further improve the QoE of its users. A distributed optimization algorithm based on distributed alternating direction method of multipliers (ADMM) via variable splitting is proposed to achieve the optimal workload allocation solution that maximizes users' QoE under the given power efficiency. We consider a fog computing platform that is supported by a wireless infrastructure as a case study to verify the performance of our proposed framework. Numerical results show that our proposed approach significantly improves the performance of fog computing networks.
Practical optimization problems frequently include uncertainty about the quality measure, for example, due to noisy evaluations. Thus, they do not allow for a straightforward application of ...traditional optimization techniques. In these settings, randomized search heuristics such as evolutionary algorithms are a popular choice because they are often assumed to exhibit some kind of resistance to noise. Empirical evidence suggests that some algorithms, such as estimation of distribution algorithms (EDAs) are robust against a scaling of the noise intensity, even without resorting to explicit noise-handling techniques such as resampling. In this paper, we want to support such claims with mathematical rigor. We introduce the concept of graceful scaling in which the run time of an algorithm scales polynomially with noise intensity. We study a monotone fitness function over binary strings with additive noise taken from a Gaussian distribution. We show that myopic heuristics cannot efficiently optimize the function under arbitrarily intense noise without any explicit noise-handling. Furthermore, we prove that using a population does not help. Finally, we show that a simple EDA called the compact genetic algorithm can overcome the shortsightedness of mutation-only heuristics to scale gracefully with noise. We conjecture that recombinative genetic algorithms also have this property.
Kernel Density Estimation (KDE) is an important approach to analyse spatial distribution of point features and linear features over 2-D planar space. Some network-based KDE methods have been ...developed in recent years, which focus on estimating density distribution of point events over 1-D network space. However, the existing KDE methods are not appropriate for analysing the distribution characteristics of certain kind of features or events, such as traffic jams, queue at intersections and taxi carrying passenger events. These events occur and distribute in 1-D road network space, and present a continuous linear distribution along network. This paper presents a novel Network Kernel Density Estimation method for Linear features (NKDE-L) to analyse the space-time distribution characteristics of linear features over 1-D network space. We first analyse the density distribution of each linear feature along networks, then estimate the density distribution for the whole network space in terms of the network distance and network topology. In the case study, we apply the NKDE-L to analyse the space-time dynamics of taxis' pick-up events, with real road network and taxi trace data in Wuhan. Taxis' pick-up events are defined and extracted as linear events (LE) in this paper. We first conduct a space-time statistics of pick-up LE in different temporal granularities. Then we analyse the space-time density distribution of the pick-up events in the road network using the NKDE-L, and uncover some dynamic patterns of people's activities and traffic condition. In addition, we compare the NKDE-L with quadrat method and planar KDE. The comparison results prove the advantages of the NKDE-L in analysing spatial distribution patterns of linear features in network space.
Ammonia (NH3) is gaining increasing interest as a carbon-free alternative fuel in engine systems. Co-firing NH3 with diesel can overcome the high auto-ignition temperature and narrow flammability ...limits of pure NH3, while exploit its advantage. This study presents the auto-ignition properties of NH3/diesel binary fuel, at various NH3 blending ratios (10%, 30% and 50%) in a rapid compression machine. Ignition delay times (IDTs) were measured spanning a temperature range of 670–910 K, pressures of 10–20 bar, and equivalence ratios of 0.5–1.5. Typical two-stage ignition and negative temperature coefficient (NTC) response were identified for the blends. Both the first-stage and the total IDTs increase with the increasing NH3 blending ratio, and there exists a non-linear inhibiting effect of NH3 fraction on IDT. A blending mechanism was then constructed based on the existing diesel mechanism and NH3 mechanism. The mechanism can predict the inhibiting effect of NH3 addition, but fails to well capture the IDTs over the whole temperature range, especially for the first-stage IDT and the NTC response. Further kinetic analysis, including species mole fraction history, brute force sensitivity analysis and reaction pathway analysis, were conducted to gain deeper insight into the auto-ignition chemistry of the blending fuel. These analyses suggest the rate parameters of reactions NH3 + OH ⇒ NH2 + H2O and NH2 + NO ⇒ N2 + H2O are critical to accurately predict IDTs, and the competition role of NH3 for OH radical inhibits the diesel low-T chain-branching sequence, which eventually leads to restraining the reactivity of the whole reaction system.
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