Dynamic Fitness Research Articles

Evolution of Virulence in Emerging Epidemics: From Theory to Experimental Evolution and Back

Abstract The experimental validation of theoretical predictions is a crucial step in demonstrating the predictive power of a model. While quantitative validations are common in infectious diseases epidemiology, experimental microbiology primarily focuses on the evaluation of a qualitative match between model predictions and experiments. In this study, we develop a method to deepen the quantitative validation process with a polymorphic viral population. We analyse the data from an experiment carried out to monitor the evolution of the temperate bacteriophage λ spreading in continuous cultures of Escherichia coli. This experimental work confirmed the influence of the epidemiological dynamics on the evolution of transmission and virulence of the virus. A variant with larger propensity to lyse bacterial cells was favoured in emerging epidemics (when the density of susceptible cells was large), but counter-selected when most cells were infected. Although this approach qualitatively validated an important theoretical prediction, no attempt was made to fit the model to the data nor to further develop the model to improve the goodness of fit. Here, we show how theoretical analysis – including calculations of the selection gradients – and model fitting can be used to estimate key parameters of the phage life cycle and yield new insights on the evolutionary epidemiology of the phage λ. First, we show that modelling explicitly the infected bacterial cells which will eventually be lysed improves the fit of the transient dynamics of the model to the data. Second, we carry out a theoretical analysis that yields useful approximations that capture at the onset and at the end of an epidemic the effects of epidemiological dynamics on selection and differentiation across distinct life stages of the virus. Finally, we estimate key phenotypic traits characterizing the two strains of the virus used in our experiment such as the rates of prophage reactivation or the probabilities of lysogenization. This study illustrates the synergy between experimental, theoretical and statistical approaches; and especially how interpreting the temporal variation in the selection gradient and the differentiation across distinct life stages of a novel variant is a powerful tool to elucidate the evolutionary epidemiology of emerging infectious diseases.

Periodate activation with polyaniline-derived carbon for bisphenol A degradation: Insight into the roles of nitrogen dopants and non-radical species formation

Periodate-based advanced oxidation processes (PI-AOPs) activated by carbon catalysts have shown promising application prospects in the removal of emerging contaminants. Herein, a series of N-doped carbon catalysts (NC) were prepared by in-situ pyrolysis of N-containing polymers (polyaniline) to unveil the potential mechanism of N dopants on PI activation. The optimized sample NC700 showed a satisfying degradation performance, achieving 100 % removal of bisphenol A (BPA) at a concentration of 10 mg/L within 90 min in the presence of 0.1 mM PI. The impacts of various N types on PI activation were systematically investigated by dynamic fitting and density functional theory computations. These analyses highlighted the crucial role of graphitic N configuration on carbon surface, which exhibited the highest adsorption energy and a positive correlation with the normalized degradation efficiency. The chemical quenching experiments and electrochemical experiments confirmed that the BPA oxidation mechanism was primarily mediated electron transfer by [NC-PI]* complex, with singlet oxygen (1O2) /superoxide radicals (O2•–) playing a supplementary role. In situ ATR-SERAS measurement strengthened the understanding of PI activation by carbon catalyst based on electron transfer pathway. Owing to the non-radical oxidation mechanism, the NC/PI system had a broad pH adaptability and great anti-interference ability against typical wastewater components. This study provides mechanistic insights into the carbon-driven PI activation process for wastewater treatment, advancing the practical application of PI-AOPs.

Dynamic and static mutual fitting for action recognition

Action recognition is intended to classify a video into a certain category by aggregating and summarizing its temporal and spatial information. Existing methods have achieved remarkable performance on the standard datasets. However, how to accurately recognize the action under occluded scenarios remains the major challenge in practical applications and is barely explored. Although previous augmentation methods have made some efforts by introducing additional modalities to deal with occlusion cases, the continuous occlusion related to temporal in the video is rarely focused. To tackle this issue, this paper takes the sample augmentation and feature representation into consideration. Specifically, we design a Dynamic Temporal-aware Erasing (DTE) augmentation strategy to enrich the occlusion samples. The proposed DTE builds upon an explicit analysis conditioned with temporal dimension along with the actors’ trajectories, which ensures the capability of carrying the temporal relation to an arbitrary spatial augmentation setting. Specifically, DTE makes the added augmentation samples more temporally consistent with obtaining the motion trajectories of the actors, enhancing the robustness against occlusions. Besides, we revisit the necessity of diverse backgrounds and propose Dynamic and Static Mutual Fitting (DSMF) to optimize the action recognition model. DSMF incorporates background auxiliary mutual fitting actors to distinguish features, which learns a smooth representation with the global temporal consistency. Extensive experiments conducted on standard benchmarks have proved that the proposed DSMF achieves competitive performance over powerful competitors.

Preparation of WO3/MoO3-x composites from W–Mo alloy scrap and it's adsorption-photocatalytic properties

A straightforward hydrothermal method was employed to fabricate WO3/MoO3 composites, with the addition of ascorbic acid (Vc) for modification, resulting in WO3/MoO3 composites with oxygen defects (WO3/MoO3-x). WO3/MoO3-x exhibits exceptional adsorption-photocatalytic properties, demonstrating strong adsorption capability for dyes achieved through hydrogen bonding and electrostatic interactions. For all products, adsorption rates for methylene blue (MB) exceeded 95 % when reaching adsorption equilibrium. Moreover, an increase in molybdenum (Mo) content endows the samples with enhanced adsorption capacity for methyl orange (MO) and improved photocatalytic performance. Dynamic fitting of the samples revealed that when the W/Mo molar ratios is 2:8, the product W2Mo8-Vc, exhibits the highest photocatalytic activity. After 3 h of illumination, W2Mo8-Vc achieves a remarkable photocatalytic efficiency of 98 % for dyes. This research not only successfully recycles W–Mo alloy scrap but also yields WO3/MoO3-x composites with adsorption-photocatalytic properties, offering a novel approach for the recycling of W–Mo secondary resource.

Efficient adsorptive separation of nitrotoluene isomers via a stable and low-cost metal–organic framework with descending affinity order of meta-/ortho-/para-isomers

Obtaining purified nitrotoluenes from their mixture is significant but challenging due to their similar molecular sizes and physical properties. To overcome this challenge, here we report a stable, inexpensive and green-synthesized aluminium-based metal–organic framework (MIL-160) to separate the nitrotoluene mixtures. The appropriate pore size and pore environment permitted MIL-160 to exhibit a rare meta-nitrotoluene (m-NT) preference and always maintain the meta- > ortho- > para-nitrotoluene (m- > o- > p-NT) adsorption order in dynamic breakthrough and pulse modes as well as batch-mode adsorption. Theoretical calculations revealed that the abundant H/O atoms in the confined nanospace provided strong binding sites for selectively capturing nitrotoluene isomers. Specifically, due to difference in shape matching, the strongest host–guest interactions occurred in m-NT@MIL-160, followed by o-NT@MIL-160, and last p-NT@MIL-160. Notably, breakthrough experiments confirmed that a variety of binary and ternary mixtures can be separated via continuous adsorption. The dynamic adsorption stage of ternary mixture directly produced 0.33 mmol g−1 of pure p-NT, and the eluent in the desorption stage contained a high content of m-NT. Additional dynamic adsorption model fitting results indicated that the outstanding dynamic separation performance was also achieved from kinetic factors. These results lay the foundation for the effective separation of nitrotoluene mixtures.

Effect of Photoperiod on Dry Matter Accumulation and Partitioning in Potato

To explore the effect of the photoperiod on the accumulation and distribution of dry matter in potato, a pot experiment was carried out in 2021 and 2022 with two varieties (Atlantic and Hezuo 88). The varieties were used as the main plot, and light treatments (short-day and long-day) were used as the subplot. The results showed that extended hours of light delayed tuber formation in Hezuo 88, however, the effect was not obvious for the Atlantic. Comprehensive analyses were carried out using the potato developmental process, dynamic equation fitting of the tuber and whole-plant dry matter accumulation, and the dry matter accumulation and distribution rate of each organ of the two varieties under two photoperiods. The two photoperiods had different effects on the parameters of rapid tuber and whole-plant dry matter accumulation: the starting point of the period of the rapid dry matter accumulation (t1), the duration period of the rapid dry matter accumulation (Δt), and the average growth rate of the period of the rapid dry matter accumulation (Vmean). According to comprehensive analysis, tuber dry matter accumulation in Atlantic was the highest under the short-day condition, while Hezuo 88 showed the lowest tuber dry matter accumulation under the long-day condition and was the latest to enter the rapid tuber dry matter accumulation period. The whole-plant dry matter accumulation in Atlantic was the highest under the long-day condition and lowest in Hezuo 88; meanwhile, Hezuo 88 was the latest to enter the rapid whole-plant dry matter accumulation period. In terms of the dry matter accumulation and dry matter partitioning ratio of various organs, Hezuo 88 had the lowest mean tuber dry matter accumulation and partitioning ratio under the long-day condition but the highest mean stem, leaf, root, underground stem, and stolon dry matter partitioning ratio. On the contrary, Atlantic had the highest mean tuber dry matter accumulation and portioning ratio under the short-day condition but the lowest mean stem, leaf, root, underground stem, and stolon dry matter partitioning ratio. It was concluded that different varieties of potato respond differently to the photoperiod. In the case of Hezuo 88, prolonging the photoperiod affected the dynamics and distribution of dry matter accumulation; increased the stem, leaf, root, and underground stem dry matter partitioning ratio; and decreased the tuber dry matter partitioning ratio, which resulted in a decrease in tuber dry matter accumulation and consequently delayed the emergence of the equilibrium period between the aboveground and underground dry matter.

DILA: Dynamic Gaussian Distribution Fitting and Imitation Learning-Based Label Assignment for tiny object detection

With the rapid advancement of deep learning technology, significant achievements have been made in the field of general object detection. However, challenges still remain in the detection of tiny objects. There are two main drawbacks: (1) static prior information makes the localization of tiny objects relatively fixed; (2) Intersection over Union (IoU) is highly sensitive to deviations in tiny objects. To this end, we propose a Dynamic Gaussian Distribution Fitting and Imitation Learning-Based Label Assignment (DILA) strategy for Tiny Object Detection. Specifically, to address the positional deviation of effective receptive fields at different network layers during Gaussian modeling, DILA first designs an Adaptive Dynamic Calculation Strategy (ADCS) to compute estimation factors for the effective receptive field in different feature spaces, dynamically modeling prior information using Gaussian distribution. Then, DILA introduces a new Balance of Gaussian Scaling-aware Metric (BGSM) to measure the similarity between tiny bounding boxes and predefined anchors, instead of using IoU, which is highly sensitive to tiny pixel shifts, for sample assignment, thereby providing a more accurate basis for label assignment. Finally, a Detail Information Imitation Compensation Module (DIM) is presented to improve and compensate for the detailed information of tiny objects that troubles label assignment, achieving balanced learning for tiny objects. The proposed DILA strategy can be seamlessly integrated into various anchor-based detectors. Extensive experiments were conducted on three publicly available datasets for tiny object detection. The results indicate that when DILA is embedded into Faster RCNN, it outperforms other state-of-the-art methods in terms of detection performance for tiny objects, achieving an improvement in average precision of 10.3%, 1.5%, and 4.2% on AI-TOD, SODA-D, and VisDrone2019, respectively. The source codes and results are available at: https://github.com/chnu-cpl/DILA.

Ecological tradeoffs lead to complex evolutionary trajectories and sustained diversity on dynamic fitness landscapes

The course and outcome of evolution are critically determined by the fitness landscape, which maps genotype to fitness. Most theory has considered static fitness landscapes or fitness landscapes that fluctuate according to abiotic environmental changes. In the presence of biotic interactions between coexisting genotypes, the fitness landscape becomes dynamic and frequency-dependent.Here, we introduce a fitness landscape model that incorporates ecological interactions between individuals in a population. In the model, fitness is determined by individuals competing for resources according to a set of traits they possess. An individual’s genotype determines the trait values through a Rough Mount Fuji fitness landscape model, allowing for tunable epistasis (i.e. non-additive gene interaction) and trait correlations (i.e. whether there are tradeoffs or synergies in the ability to use resources). Focusing on the effects of epistasis and trait correlations, we quantify the resulting eco-evolutionary dynamics under simulated Wright–Fisher dynamics (i.e. including genetic drift, mutation, and selection under the assumption of a constant population size) on the dynamics fitness landscape in comparison with a similar, static, fitness landscape model without ecological interactions.Whereas the non-ecological model ultimately leads to the maintenance of one main genotype in the population, evolution in the ecological model can lead to the long-term coexistence of several genotypes at intermediate frequencies across much of the parameter range. Including ecological interactions increases steady-state diversity whenever the trait correlations are not too strong. However, strong epistasis can hinder coexistence, and additive genotype–phenotype maps yield the highest haplotype diversity at the steady state. Interestingly, we frequently observe long-term coexistence also in the absence of induced trade-offs in the ability to consume resources.In summary, our simulation study presents a new dynamic fitness landscape model that highlights the complex eco-evolutionary consequences of a (finite) genotype–phenotype-fitness map in the presence of biotic interactions.

A pareto fronts relationship identification-based two-stage constrained evolutionary algorithm

Striking a balance between diverse constraints and conflicting objectives is one of the most crucial issues in solving constrained multi-objective optimization problems (CMOPs). However, it remains challenging to existing methods, due to the reduced search space caused by the constraints. For this issue, this paper proposes a Pareto fronts relationship identification-based two-stage constrained evolutionary algorithm called RITEA, which balances objective optimization and constraint satisfaction by identifying and utilizing the relationship between the unconstrained Pareto front (UPF) and the constrained Pareto front (CPF). Specifically, the evolutionary process is divided into two collaborative stages: training stage and reinforcement stage. In the training stage, a relationship identification method is developed to estimate the relationship between UPF and CPF, which guides the population search direction. In the reinforcement stage, the corresponding evolutionary strategies are designed based on the identified relationship to enhance the accurate search on the CPF. Furthermore, a dynamic preference fitness function (termed DPF) is designed to adaptively maintain the balance of search preference between convergence and diversity. Compared to seven state-of-the-art algorithms on 36 benchmark CMOPs in three popular test suites, RITEA obtains 77.8% of the best IGD values and 66.7% of the best HV values. The experimental results show that RITEA exhibits highly competitively when dealing with CMOPs.

Accuracy and feasibility of 3D virtual dynamic fit technology

PurposeThe research evaluated the feasibility of 3D dynamic fit utilizing female compression tops by comparatively analyzing the virtual and actual dynamic fit.Design/methodology/approachSix female participants were 3D body-scanned and photographed in compression tops in four types of athletic movements (pull-up, kettlebell swing, circle-crunch and sit-up). Fit measurements, waist cross-sectional areas, waist width, waist depth, numerical simulation of clothing pressure (kPa) and objective pressure measurements (kPa) were collected from 3D virtual animation, 3D fit scan data and actual photos with the four types of athletic motions. The data were comparatively investigated between virtual and actual dynamic fit.FindingsThe 3D-animated body was not reflected with human body deformation because only bone structure was changed while maintaining the constant forms of muscle and body surface in athletic movements. Due to this consistency of virtual dynamic fit, there were significant differences with the actual dynamic fit at the top length, shoulder width and waist cross-sectional areas. Also, the virtual dynamic pressure indicated significantly higher levels than the objective dynamic pressure while presenting no significant correlations at the front neckline, breast, lateral waist, upper back, back armhole and back waist.Originality/valueThis study is the first to verify multiple aspects of virtual dynamic fit using 3D digital technology. This study provided useful information about which aspects of the current virtual animation need to be improved to apply in the dynamic fit evaluation.

A Dynamic Fitting Strategy for Physiological Models: A Case Study of a Cardiorespiratory Model for the Simulation of Incremental Aerobic Exercise.

Using mathematical models of physiological systems in medicine has allowed for the development of diagnostic, treatment, and medical educational tools. However, their complexity restricts, in most cases, their application for predictive, preventive, and personalized purposes. Although there are strategies that reduce the complexity of applying models based on fitting techniques, most of them are focused on a single instant of time, neglecting the effect of the system's temporal evolution. The objective of this research was to introduce a dynamic fitting strategy for physiological models with an extensive array of parameters and a constrained amount of experimental data. The proposed strategy focused on obtaining better predictions based on the temporal trends in the system's parameters and being capable of predicting future states. The study utilized a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects undergoing aerobic exercise were used for fitting and validation. The model predictions obtained in a steady state using the proposed strategy and the traditional single-fit approach were compared. The most successful outcomes were primarily linked to the proposed strategy, exhibiting better overall results regarding accuracy and behavior than the traditional population fitting approach at a single instant in time. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications.

Optimization and Performance Evaluation of Artificial Intelligence Algorithms in Game Design

This article aims to optimize artificial intelligence algorithms in game design to enhance game performance and player experience. Therefore, an innovative method called Dynamic Game AI Fitness Optimization Algorithm (DGAFOA) was proposed in the article, and its effectiveness was verified through experiments. In the experimental section, this article applies the DGAFOA algorithm to a typical role-playing game and compares it with traditional fixed parameter AI algorithms. The experimental results show that the DGAFOA algorithm exhibits significant advantages in key indicators such as game task completion rate and player satisfaction. Specifically, game AI using the DGAFOA algorithm can respond more quickly and accurately to player behavior, improving the overall smoothness and fun of the game. In addition, the DGAFOA algorithm also introduces ε- The greedy strategy balances exploration and utilization, effectively avoiding the problem of AI getting stuck in local optima. This enables game AI to maintain stable performance while still possessing the ability to explore new strategies, bringing players more surprises and challenges.

Direct Discrepancy Dynamic Fit Index Cutoffs for Arbitrary Covariance Structure Models

Despite the popularity of traditional fit index cutoffs like RMSEA ≤ .06 and CFI ≥ .95, several studies have noted issues with overgeneralizing traditional cutoffs. Computational methods have been proposed to avoid overgeneralization by deriving cutoffs specifically tailored to the characteristics of the model being evaluated. Simulations show favorable performance of these methods; however, these methods support a narrow set of scenarios (e.g., certain models or response scales) and the interpretation of cutoffs is not always standardized, which affects empirical researchers’ ability to confidently and broadly adopt these methods to evaluate model fit. In this paper, we propose an extension to one recently developed computational method—dynamic fit index cutoffs—that (a) permits application to any covariance structure model (e.g., CFA, mediation, bifactor), (b) standardizes interpretation of cutoffs across any covariance structure model, and (c) supports normal, nonnormal, categorical, and missing data. Software is provided to facilitate implementation of the method.

Activation of periodate by biocarbon-supported multiple modified nanoscale iron for the degradation of bisphenol A in high-temperature aqueous solution.

As reported, the persistent toxic and harmful pollutant bisphenol A (BPA) from industrial emissions has been consistently found in aquatic environments inhabited by humans. Periodate (PI)-based advanced oxidation processes (AOPs) have been employed to degrade BPA, although activating PI proves more challenging compared to other oxidants. A novel nano iron metal catalyst, sulfided nanoscale iron-nickel bimetallic nanoparticle supported on biocarbon (S-(nFe0-Ni)/BC) was synthesized and utilized to activate PI for the removal of BPA. The morphology, structure, and composition of S-(nFe0-Ni)/BC were characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy-energy dispersive spectrometer (SEM-EDS), and fourier-transform infrared spectrum (FTIR). The catalyst demonstrates an excellent ability to activate PI, achieving a BPA removal efficacy of 86.4%, accompanied by a 33% reduction in total organic carbon (TOC) in the {S-(nFe0-Ni)/BC}/PI system. BPA degradation exhibited a significant change at the 5-min mark. In the first stage (0-5min), nonlinear dynamic fitting research, combined with scavenging experiments, unveiled the competitive degradation of pollutants primarily driven by iodate radical ( ), singlet oxygen , and hydroxyl radical ( ). The competitive dynamics aligned with the ExpAssoc model. The contribution rates of different active species during the second stage (5-120min) were calculated. The contributions of main species to BPA removal follow the order of > > throughout the entire process. The influence of various parameters, such as the dosage of S-(nFe0-Ni)/BC, initial PI concentration, BPA concentration, pH, temperature, and the presence of coexisting anions, was also examined. Finally, a plausible reaction mechanism in the system is proposed, suggesting that the {S-(nFe0-Ni)/BC}/PI system involves a heterogeneous synergistic reaction occurring primarily on the surface of S-(nFe0-Ni)/BC. Therefore, this study proposes a promising approach for PI-based AOPs to degrade organic pollutants, aiming to mitigate the irreversible harm caused by such pollutants to organisms and the environment.

Research on the data-driven inter-well fracture channeling identification method for shale gas reservoirs

The issue of inter-well fracture channeling in shale reservoirs is becoming increasingly prominent, significantly impacting the production of nearby wells. Therefore, it is crucial to accurately determine the location of fracture channeling in order to effectively design anti-channeling measures and optimize reservoir fracturing. In this paper, a data-driven fracture propagation model and fracture channeling identification method are established. In the model, the fracture morphology is fitted by the bottom-hole flowing pressure constraint. The bottom-hole flowing pressure (pwp) calculated by the construction pump pressure and the fluid wellbore flow is mainly considered as the real solution. The bottom-hole flowing pressure (pwf) calculated by the construction displacement and the fracture morphology is used as the constraint variable, and the fracture parameters are changed using the SPSA optimization algorithm to realize the dynamic fitting of the fracture morphology. In order to accurately describe the position of fracture channeling, the seepage radius of the fracture boundary is introduced to calculate the volume of fracture reconstruction. The volume coefficient of repeated reconstruction is used as the quantitative evaluation index of fracture channeling. This approach enables an accurate depiction of the position of fracture channeling. Finally, the model method is applied to the actual fracture channeling well. The study shows that the fracture length of the well inversion is greater than the well spacing, and there is a possibility of inter-well fracture channeling. The volume coefficient of repeated reconstruction is 8%, similar to the critical fracture channeling index. There are nine fracturing sections with fracture channeling, and the maximum fracture channeling coefficient is 14.2%. This paper successfully explains the reason for cross-well fracture channeling, and its conclusion aligns with the actual monitoring results. The proposed method in this paper effectively identifies the location of fracture channeling and offers guidance for optimizing channeling prevention in subsequent designs.

AI Fitness Model using Deep Learning

The project “AI Fitness Model Using Deep Learning (YOLOv5)” aims inorder to transform the fitness business by leveraging state-of-the-art deep learning techniques, specifically YOLOv5 (You Only Look Once version 5), to develop an advanced and efficient fitness tracking system. The model is designed to accurately detect and analyze human poses, movements, and exercise routines in real-time using computer vision. By employing YOLOv5’s object detection capabilities, the AI fitness model can identify key body points, track exercise execution, and provide personalized feedback to users, enhancing their workout experience. This creative strategy not only facilitates automated performance monitoring but also enables the creation of adaptive and dynamic fitness routines tailored to individual needs, fostering improved engagement and results in the realm of health and wellness

Universal dynamic fitting of magnetic resonance spectroscopy.

Dynamic (2D) MRS is a collection of techniques where acquisitions of spectra are repeated under varying experimental or physiological conditions. Dynamic MRS comprises a rich set of contrasts, including diffusion-weighted, relaxation-weighted, functional, edited, or hyperpolarized spectroscopy, leading to quantitative insights into multiple physiological or microstructural processes. Conventional approaches to dynamic MRS analysis ignore the shared information between spectra, and instead proceed by independently fitting noisy individual spectra before modeling temporal changes in the parameters. Here, we propose a universal dynamic MRS toolbox which allows simultaneous fitting of dynamic spectra of arbitrary type. A simple user-interface allows information to be shared and precisely modeled across spectra to make inferences on both spectral and dynamic processes. We demonstrate and thoroughly evaluate our approach in three types of dynamic MRS techniques. Simulations of functional and edited MRS are used to demonstrate the advantages of dynamic fitting. Analysis of synthetic functional 1H-MRS data shows a marked decrease in parameter uncertainty as predicted by prior work. Analysis with our tool replicates the results of two previously published studies using the original in vivo functional and diffusion-weighted data. Finally, joint spectral fitting with diffusion orientation models is demonstrated in synthetic data. A toolbox for generalized and universal fitting of dynamic, interrelated MR spectra has been released and validated. The toolbox is shared as a fully open-source software with comprehensive documentation, example data, and tutorials.

INTaaS: Provisioning In-band Network Telemetry as a service via online learning

Provisioning In-band Network Telemetry as a service for INT-based and INT follow-up applications in an online manner suffers multiple challenges, including control decisions of different INT path algorithms, resources provisioning coupled over time, and the unforeseeable INT query workloads. To overcome these challenges, this study formulates an online non-linear time-varying integer programming problem that maximizes the overall quality of service through algorithm selection and INT query workloads distribution. Specifically, an online learning algorithm is proposed to make the fractional decisions and it uses a primal–dual mechanism by simultaneously minimizing a convex problem and maximizing a concave problem, based on the previously observed inputs. Furthermore, we design a randomized rounding strategy to convert these fractional decisions to integers with an expectation guarantee. We implement our system prototype based on INTCollector upon real devices, i.e., Barefoot Wedge100BF and Inspur Rack. Our rigorous theoretical analysis shows that our INTaaS achieves sub-linear growth on dynamic regret for the optimal loss and incurs sub-linear growth on dynamic fit for the long-term constraint violations. Finally, extensive evaluations on real-world data indicate that INTaaS exhibits up-lift performance up to 40% over other state-of-the-art algorithms.

AQMon: A Fine-grained Air Quality Monitoring System Based on UAV Images for Smart Cities

Air quality monitoring is important to the green development of smart cities. Several technical challenges exist for intelligent, high-precision monitoring, such as computing overhead, area division, and monitoring granularity. In this article, we propose a fine-grained air quality monitoring system based on visual inspection analysis embedded in unmanned aerial vehicle (UAV), referred to as AQMon . This system employs a lightweight neural network to obtain an accurate estimate of atmospheric transmittance in visual information while reducing computation and transmission overhead. Considering that air quality is affected by multiple factors, we design a dynamic fitting approach to model the relationship between scattering coefficients and PM2.5 concentration in real time. The proposed system is evaluated using public datasets and the results show that AQMon outperforms four existing methods with a processing time of 13.8 ms.

Situating implementation science (IS) in res(IS)tance: a conceptual frame toward the integration of scholarship from the black radical tradition.

This manuscript undertakes a disciplinary self-critique of the field of implementation science, a field which attempts to bridge the gap between evidence-based interventions and their practical application. Despite the heightened emphasis on health equity and racial disparities, the field's current discourse is limited by key epistemic shortcomings. First, even though prevalence of implementation gaps between racialized groups in the United States necessitates a comprehensive understanding of the systems perpetuating these disparities, the field does not operate with a general explanation for disparities not as a failure of systems, but a system historically and structural designed to produce disparities. Second, the field has attempted to address disparities without adequate dialog with a broad tradition of anti-racist and anti-colonial sociology, history and epistemology, and therefore risks a decontextualized analysis of disparities and under-informed approaches to achieving equity. Fortunately, scholarship from the Black radical tradition (BRT), such as the Public Health Critical Race Praxis (PHCRP), Critical Race Theory (CRT), and more broadly conceptual frameworks from post-modern, anti-colonial, Black feminist studies and social epistemology can offer to implementation science frameworks that center power dynamics and racialized oppression. This epistemic re-alignment of implementation research to "center at the margins" can enable the field of implementation science to more critically examine and dismantle systems that perpetuate racial inequalities in access to and utilization of health interventions. For example, normalization and dynamic fit, which are thought to be key mechanisms of implementation, are revealed in the light of this tradition of scholarship to be potentially problematic acquiescence to oppressive systems. Drawing from the concept of resistance anchored in the scholarship of the Black radical tradition as well as contemporary social epistemology such as the work of José Medina and Maria Fricker about epistemic justice, the authors further advance that implementation science could make more substantial contributions to the dismantling of racialized systems and actively work toward health justice through the transdisciplinary lens of resistance. This is a call to action for integrating implementation science with critical philosophical and theoretical perspectives rooted in Black studies and related insights, which have been acquired through the struggle for social justice, to inform the design of implementation strategies and research projects that improve health services and health outcomes for health disparity populations.