Time-varying Environmental Factors Research Articles

E3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology.

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogen's genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIM's capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

Distributed Data Flow Scheduling Optimization in Industrial Internet of Things Based on Optimal Transport Theory

The development of industrial internet of things (IIoT) has completely changed the traditional manufacturing industry. The data exchange between controllers and actuators needs to achieve extremely low delay in IIoT. Due to the limited communication resources, it is necessary to reasonably schedule data flow to reduce delay. Although the studies of data flow scheduling exist in IIoT, they have not considered the impact of time-varying environmental factors and most of them adopted centralized scheduling schemes, which increase computation and communication cost rapidly in large-scale network scenarios. In this paper, the consensus-based distributed optimal transport (OT) algorithm is proposed to optimize data flow scheduling for IIoT networks. Specifically, a data flow scheduling optimization mechanism based on time-varying environmental factors is proposed and an online distributed data flow scheduling optimization algorithm is designed. Compared with the random data flow scheduling algorithm, numerical results show that the proposed algorithm can maximally reduce the average delay by 87%, increase the transmission rate and the spectral efficiency by 157% and 98%, respectively.

Environmental information-aided electromagnetic propagation testbed for maritime communication

Maritime channel modeling can be affected by some key time-varying environmental factors. The ducting effect is one of the thorniest factors since it causes anomalous propagation enhancement and severe co-channel interference. Moreover, the atmospheric attenuation is much more severe in the ocean environment, resulting in shorter coverage distance and more link outage. In this paper, we propose an environmental information-aided electromagnetic propagation testbed. It is based on complex refractivity estimation and improved parabolic equation propagation model, taking into account both ducting effect and atmospheric attenuation. A large-scale temporal and spatial propagation measurement was conducted with meteorological acquisition. We consider practical path loss and ducting conditions to verify the testbed feasibility in these long-distance radio links. The simulation results are in good agreement with the measured data, which further reveal the basic temporal and spatial distribution of ducting effect at 3.5 GHz band.

An Energy Efficiency Optimization Strategy of Hybrid Electric Ship Based on Working Condition Prediction

Optimizing the operational performance of green ships can further improve the energy saving and emission reduction effect of ships, and speed optimization is one of the more widely used and effective measures. It is a new challenge for the shipping industry to achieve speed optimization that simultaneously saves energy, reduces emissions and meets transportation requirements, while considering changes in the navigation environment. In this paper, a hybrid electric ship energy efficiency optimization strategy based on working condition prediction is proposed to solve the problem of navigation condition at a future moment, by making a time series prediction of energy efficiency influencing factors, such as wind speed and current speed. Further, on the basis of establishing the sailing speed prediction model and the real-time energy efficiency operation index (EEOI) model, the real-time EEOI deviation and the sailing speed deviation are adopted as the comprehensive objective function to establish a dynamic optimization model of hybrid electric ship energy efficiency, considering the time-varying environmental factors. Then, the fast Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is applied to solve the bi-objective optimization problem and obtain the optimal ship engine speed in real time. Finally, experimental studies show that the proposed optimization model can improve the energy-saving and emission-reduction effect of the ship under the given speed limit requirements and working environment conditions, which can provide theoretical support for the optimal navigation of hybrid electric ships.

Complex trait methylation scores in the prediction of major depressive disorder.

SummaryBackgroundDNA methylation (DNAm) is associated with time-varying environmental factors that contribute to major depressive disorder (MDD) risk. We sought to test whether DNAm signatures of lifestyle and biochemical factors were associated with MDD to reveal dynamic biomarkers of MDD risk that may be amenable to lifestyle interventions.MethodsHere, we calculated methylation scores (MS) at multiple p-value thresholds for lifestyle (BMI, smoking, alcohol consumption, and educational attainment) and biochemical (high-density lipoprotein (HDL) and total cholesterol) factors in Generation Scotland (GS) (N=9,502) and in a replication cohort (ALSPACadults, N=565), using CpG sites reported in previous well-powered methylome-wide association studies. We also compared their predictive accuracy for MDD to a MDD MS in an independent GS sub-sample (N=4,432).FindingsEach trait MS was significantly associated with its corresponding phenotype in GS (βrange=0.089–1.457) and in ALSPAC (βrange=0.078–2.533). Each MS was also significantly associated with MDD before and after adjustment for its corresponding phenotype in GS (βrange=0.053–0.145). After accounting for relevant lifestyle factors, MS for educational attainment (β=0.094) and alcohol consumption (MSp-value<0.01–0.5; βrange=-0.069–0.083) remained significantly associated with MDD in GS. Smoking (AUC=0.569) and educational attainment (AUC=0.585) MSs could discriminate MDD from controls better than the MDD MS (AUC=0.553) in the independent GS sub-sample. Analyses implicating MDD did not replicate across ALSPAC, although the direction of effect was consistent for all traits when adjusting for the MS corresponding phenotypes.InterpretationWe showed that lifestyle and biochemical MS were associated with MDD before and after adjustment for their corresponding phenotypes (pnominal<0.05), but not when smoking, alcohol consumption, and BMI were also included as covariates. MDD results did not replicate in the smaller, female-only independent ALSPAC cohort (NALSPAC=565; NGS=9,502), potentially due to demographic differences or low statistical power, but effect sizes were consistent with the direction reported in GS. DNAm scores for modifiable MDD risk factors may contribute to disease vulnerability and, in some cases, explain additional variance to their observed phenotypes.FundingWellcome Trust.

Evolution of cooperation in public goods games with segregated networks and periodic invasion

Despite the cost, rational individuals routinely modify their relationships in social networks to optimize their payoffs. To develop this observation, we propose a segregation strategy that impels individuals to sever the adverse ties between defectors and their group at some cost, and then we randomly rewire the link-broken cooperative individuals. Moreover, time-varying environmental factors impact the relationships between interacting individuals, in particular certain periodic changes such as the circadian rhythm and seasonality, thus affecting the evolution of cooperation in spatial public goods games. By this means, periodic invasion is taken into account, which allows defectors with lower degrees than the initial value to randomly restore links and invade the population again. Through simulation, we find that the segregation strategy involving periodic invasion can effectively facilitate cooperation by formatting assortative and heterogeneous topology. Furthermore, we observe a subtle interplay between the costly topology control strategy and the exogenous changes in temporal factors that alter the spatiotemporal dynamics of coevolution and promote cooperation. We conclude that a relatively low periodic invasion frequency contributes to increasing the level of cooperation, but the threshold of cooperation is not sensitive to the change in invasion period when at longer intervals. We present our interpretations of these phenomena, which thus allow us to better understand the role of this new machine in the emergence of cooperation.

Quantitative prediction of dynamic HTO migration behavior in the soil and non-negligible evapotranspiration effect

Tritium is mainly produced from nuclear facilities apart from nuclear tests. After being released to the environment, tritium would cause water & food contamination due to its radioactivity and mobility. This study investigated dynamic characteristics of tritiated water (HTO) migration in the soil and evapotranspiration effect based on realistic environmental conditions. The influences of soil types and time-varying environmental factors such as precipitation and evapotranspiration on tritium migration behaviors were specially discussed under normal continuous and accidental short-term release conditions. Radiation dose caused by dynamic tritium evapotranspiration was evaluated. The results show that tritium migration velocity in the soil is much higher than other particles such as cesium due to negligible adsorption of tritium by the soil. Tritium migration in the soil from up to down is attributed to precipitation. On the contrary, evapotranspiration factor would carry tritium movement along the opposite direction. A considerable fraction approximately 55% of tritium deposited in the soil would be reemitted into the air from bare soil and plant leaves due to evapotranspiration effect. Subsequently, the radiation dose caused by second plume due to evapotranspiration effect might be higher than the first plume due to direct release from the nuclear facility under routine discharge.

Stochastic prognostics under multiple time-varying environmental factors

Prediction of the remaining useful life of in-field components, traditionally, relies on condition monitoring signals which are correlated with the physical degradation of the system. Many models assume that condition monitoring signals behave under similar environmental conditions (e.g. pressure, temperature, workload and relative humidity) or these conditions have no effect on degradation process. In this paper, we propose a Brownian motion process with a stress-dependent drift to model multiple time-varying environmental covariates. A semiparametric regression approach utilizing penalized splines is, further, proposed to model the environmental covariates-drift relationship. The unique feature of our approach is that it does not assume a functional form for the degradation process drift and models multiple environmental covariates’ effect on the degradation process. Moreover, the model is combined with in situ degradation measurements of the in-field unit and its environmental conditions to predict the unit’s remaining useful life through a Bayesian updating scheme. The performance of the proposed framework is investigated and benchmarked through analysis based on numerical studies and a case study using real-world data of frying oil degradation collected from connected fryers.

Online Residential Demand Response via Contextual Multi-Armed Bandits

Residential loads have great potential to enhance the efficiency and reliability of electricity systems via demand response (DR) programs. One major challenge in residential DR is how to learn and handle unknown and uncertain customer behaviors. In this letter, we consider the residential DR problem where the load service entity (LSE) aims to select an optimal subset of customers to optimize some DR performance, such as maximizing the expected load reduction with a financial budget or minimizing the expected squared deviation from a target reduction level. To learn the uncertain customer behaviors influenced by various time-varying environmental factors, we formulate the residential DR as a contextual multi-armed bandit (MAB) problem, and develop an online learning and selection (OLS) algorithm based on Thompson sampling to solve it. This algorithm takes the contextual information into consideration and is applicable to complicated DR settings. Numerical simulations are performed to demonstrate the learning effectiveness of the proposed algorithm.

Channel Estimation Based on Adaptive Denoising for Underwater Acoustic OFDM Systems

Underwater acoustic (UWA) communications systems suffer from a low signal-to-noise ratio (SNR) and a doubly selective channel, which are caused by many limitations, such as high propagation loss, slow propagation speed, and time-varying environmental factors. To overcome a low SNR, various diversity techniques are often adopted in UWA communications. However, such diversity can only be exploited with knowledge of the channel. Accordingly, channel estimation needs to be performed without obtaining the benefit of diversity. Moreover, accurate side information that can support a channel estimator (CE) is difficult to acquire at a low SNR under doubly selective channel. In this article, a novel CE based on an adaptive denoising is proposed for UWA orthogonal frequency-division multiplexing (OFDM) systems. The proposed method exploits two different types of pilot symbols. Channel impulse response (CIR) is estimated based on primary pilot symbols. By minimizing the squared error of the received secondary pilot symbols, a near-optimal denoising window is adaptively determined based on the channel length for the given CIR estimate. The proposed method does not require a priori information about channel statistics and SNR values. Analysis on the effect of denoising and the performance of the proposed denoising window estimator are also presented. Simulation and at-sea experiments verify that the proposed method has superior performance, compared with conventional CEs over diverse channel conditions. Complexity analysis shows that the proposed method is computationally efficient. Therefore, the proposed method is effective for real-time UWA OFDM systems under a harsh UWA channel with strong noise.

Error-free secure key generation and distribution using dynamic Stokes parameters.

This paper proposes and experimentally demonstrates an error-free secure key generation and distribution (SKGD) scheme in classical optical fiber link by exploiting Stokes parameters (SPs) of the state of polarization (SOP). Due to the unique birefringence distribution of the optical fiber channel, random but high-correlated SPs are shared between Alice and Bob. The dynamic SPs are also affected by the time-varying environmental factors, providing the source of randomness for the secret key extraction. As a proof of concept, key generation rate (KGR) of 213-bits/s is successfully demonstrated over 25-km standard single-mode fiber (SSMF). The error-free SKGD is realized in fiber channel using the information reconciliation (IR) technology, where Bose-Chaudhuri-Hocquenghem (BCH) codes are applied. Due to the channel uniqueness and the high-sensitivity to the initial SOP of optical signals, high-level security is provided by the proposed scheme, which is analyzed and verified against the possible fiber-tapping attacks. Moreover, the proposed SKGD scheme offers additional benefits such as simple structure, low cost, and suitablity for long-haul transmission.

Investigation of the feasibility of POE methodology for a modern commercial office building

Post-Occupancy Evaluation (POE), as an architectural and technical tool, has been researched for decades to support an effective design decision process and also for indoor environmental quality control, such as thermal, air, lighting, and acoustic quality, as well as spatial quality. However, a one-time data collection has been frequently adopted that includes survey and field measurements, even though these data acquisition approaches have been moderately changed and revised. For modern office buildings, where a large number of time-varying architectural and environmental parameters are available, and the demands of individual occupants for specific environmental conditions vary per season, day, or even hour, little research effort has been made to validate whether the one-time data collection-based POE is still effective in a modern structure. To address this challenge, this research project included four different POE studies in two commercial office buildings, in Southern California in the U.S. At 189 individual workstations, selected in two buildings, an environmental satisfaction survey and spot measurements were repeatedly made during 4 seasons while considering seasonal and daily factors. This study found that human factors, such as gender and age, contributed to inconsistent environmental perceptions, depending on the season and/or month. This research revealed that multiple-time data collection is required to provide a vigorous environmental design solution which would enhance building occupants' environmental comfort, while considering time-varying environmental factors, such as a season.

Dynamic optimization of ship energy efficiency considering time-varying environmental factors

Nowadays, optimization of ship energy efficiency attracts increasing attention in order to meet the requirement for energy conservation and emission reduction. Ship operation energy efficiency is significantly influenced by environmental factors such as wind speed and direction, water speed and depth. Owing to inherent time-variety and uncertainty associated with these various factors, it is very difficult to determine optimal sailing speeds accurately for different legs of the whole route using traditional static optimization methods, especially when the weather conditions change frequently over the length of a ship route. Therefore, in this paper, a novel dynamic optimization method adopting the model predictive control (MPC) strategy is proposed to optimize ship energy efficiency accounting for these time-varying environmental factors. Firstly, the dynamic optimization model of ship energy efficiency considering time-varying environmental factors and the nonlinear system model of ship energy efficiency are established. On this basis, the control algorithm and controller for the dynamic optimization of ship energy efficiency (DOSEE) are designed. Finally, a case study is carried out to demonstrate the validity of this optimization method. The results indicate that the optimal sailing speeds at different time steps could be determined through the dynamic optimization method. This method can improve ship energy efficiency and reduce CO2 emissions effectively.

A real-time ship roll motion prediction using wavelet transform and variable RBF network

Real-time prediction of ship roll motion is vital for marine safety and efficiency of operations onboard the ship. However, ship roll motion is a complex time-varying nonlinear process which varies with various sailing conditions as well as time-varying environmental factors. To achieve precise real-time ship roll prediction, an ensemble prediction scheme is constructed by combining the discrete wavelet transform (DWT) method with the variable-structure radial basis function (RBF) network. The DWT is used to reduce the time-series data redundancies and carry the data information in few significant uncoupled sub-series, thus facilitate the identification and prediction by using the variable RBF networks. The variable RBF networks are used to represent time-varying dynamics with both the structure and parameters are tuned in real time. The DWT-transform-based variable RBF networks are used to represent the time-varying nonlinear dynamics of ship roll movement during ship maneuvering. The effectiveness of the proposed DWT-based real-time roll prediction scheme is demonstrated by short-term ship roll motion prediction experiments based on the actual ship roll motion measurements collected during sea test of M.V. YuKun.

Modeling and state of charge estimation of lithium-ion battery

Modeling and state of charge (SOC) estimation of lithium-ion (Li-ion) battery are the key techniques of battery pack management system (BMS) and critical to its reliability and safety operation. An auto-regressive with exogenous input (ARX) model is derived from RC equivalent circuit model (ECM) due to the discrete-time characteristics of BMS. For the time-varying environmental factors and the actual battery operating conditions, a variable forgetting factor recursive least square (VFFRLS) algorithm is adopted as an adaptive parameter identification method. Based on the designed model, a SOC estimator using cubature Kalman filter (CKF) algorithm is then employed to improve estimation performance and guarantee numerical stability in the computational procedure. In the battery tests, experimental results show that CKF SOC estimator has a more accuracy estimation than extended Kalman filter (EKF) algorithm, which is widely used for Li-ion battery SOC estimation, and the maximum estimation error is about 2.3%.

Ambient Particulate Air Pollution, Environmental Tobacco Smoking, and Childhood Asthma: Interactions and Biological Mechanisms

Ambient Particulate Air Pollution, Environmental Tobacco Smoking, and Childhood Asthma: Interactions and Biological Mechanisms

Model approaches for estimating the influence of time-varying socio-environmental factors on macroparasite transmission in two endemic regions

The environmental determinants of vector- and host-borne diseases include time-varying components that modify key transmission parameters, resulting in transient couplings between environmental phenomena and transmission processes. While some time-varying drivers are periodic in nature, some are aperiodic, such as those that involve episodic events or complex patterns of human behavior. Understanding these couplings can allow for prediction of periods of peak infection risk and ultimately presents opportunities for optimizing intervention selection and timing. Schistosome macroparasites of humans exhibit multiple free-living stages as well as intermediate hosts and are thus model organisms for illustrating the influence of environmental forcing on transmission. Time-varying environmental factors, termed gating functions, for schistosomes include larval response to temperature and rainfall, seasonal water contact patterns, and snail population dynamics driven by weather variables. The biological bases for these modifiers are reviewed, and their values are estimated and incorporated into a transmission model that simulates a multiyear period in two schistosomiasis endemic regions. Modeling results combined with a scale-dependent correlation analysis indicate the end effect of these site-specific gating functions is to strongly govern worm burden in these communities, in a manner particularly sensitive to the hydrological differences between sites. Two classes of gating functions were identified, those that act in concert to modify human infection (and determine worm acquisition late in the season) and those that act on snail infection (and determine early season worm acquisition). The importance of these factors for control programs and surveillance is discussed.

Long-term change in the abyssal NE Atlantic: The ‘Amperima Event’ revisited

The results from a time series study (1989–2005) at a depth of 4850 m on the Porcupine Abyssal Plain, NE Atlantic, are presented, showing radical changes in the density of large invertebrates (megafauna) over time. Major changes occurred in a number of different taxa between 1996 and 1999 and then again in 2002. One species of holothurian, Amperima rosea, was particularly important, increasing in density by over three orders of magnitude. There were no significant changes in total megafaunal biomass during the same period. Peaks in density were correlated to reductions in mean body size, indicating that the increases were related to large-scale recruitment events. The changes occurred over a wide area of the Porcupine Abyssal Plain. Comparisons made with changes in the density of protozoan and metazoan meiofauna, and with macrofauna, showed that major changes in community structure occurred in all size fractions of the benthic community at the same time. This suggests that the faunal changes were driven by environmental factors rather than being stochastic population imbalances of one or two species. Large-scale changes in the flux of organic matter to the abyssal seafloor have been noted in the time series, particularly in 2001, and may be related to the sudden mass occurrence of A. rosea the following year. Time-varying environmental factors are important in influencing the occurrence of megafauna on the abyssal seafloor.

Energy-efficient neighbor discovery protocol for mobile wireless sensor networks

Low energy consumption is a critical design requirement for most wireless sensor network (WSN) applications. Due to minimal transmission power levels, time-varying environmental factors and mobility of nodes, network neighborhood changes frequently. In these conditions, the most critical issue for energy is to minimize the transactions and time consumed for neighbor discovery operations. In this paper, we present an energy-efficient neighbor discovery protocol targeted at synchronized low duty-cycle medium access control (MAC) schemes such as IEEE 802.15.4 and S-MAC. The protocol effectively reduces the need for costly network scans by proactively distributing node schedule information in MAC protocol beacons and by using this information for establishing new communication links. Energy consumption is further reduced by optimizing the beacon transmission rate. The protocol is validated by performance analysis and experimental measurements with physical WSN prototypes. Experimental results show that the protocol can reduce node energy consumption up to 80% at 1–3 m/s node mobility.

Haplotype-based association analysis in cohort studies of unrelated individuals.

Exploring the associations between haplotypes and disease phenotypes is an important step toward the discovery of genes that influence complex human diseases. When unrelated subjects are sampled, haplotypes are often ambiguous because of the unknown gametic phase of the measured sites along a chromosome. We consider cohort studies of unrelated subjects which collect data on potentially censored ages of onset of disease along with unphased genotypes and possibly time-varying environmental factors. We formulate the effects of haplotypes and environmental variables on the time to disease occurrence through a semiparametric Cox proportional hazards model, which can accommodate a variety of genetic mechanisms as well as gene-environment interactions. We develop a simple and fast expectation-maximization algorithm to maximize the likelihood for the relative risks and other parameters based on the observable data of unphased genotypes and potentially censored ages of onset. The resultant estimators are consistent, efficient, and asymptotically normal. Simulation studies show that, for practical situations, the parameter estimators are virtually unbiased, the association tests maintain type I errors near nominal levels, the confidence intervals have proper coverage probabilities, and the efficiency loss due to unknown gametic phase is small.