HUM: A review of hydrochemical analysis using ultraviolet-visible absorption spectroscopy and machine learning.

There is a need to develop improved methods for water quality analysis. Traditionally, water quality analysis is performed in a laboratory on discrete samples or in the field with simple sensors, but these methods have inherent limitations. Ultraviolet-visible absorption spectroscopy (UVAS) is a commonly used laboratory technique for water quality analysis and is being applied more broadly in combination with machine learning (ML) to allow for the detection of multiple analytes without sample pretreatments. This methodology (referred to here as Hydrochemical analysis using Ultraviolet-visible absorption spectroscopy and Machine learning; 'HUM') can be applied in the laboratory or in situ while requiring less time, labor, and materials compared to traditional laboratory analysis. HUM has been used for the quantification of a variety of chemicals in a variety of settings, but information is lacking related to instrumental setup, sample requirements, and data analysis procedures. For instance, there is a need to investigate the influence of spectral parameters (e.g., sensitivity, signal-to-noise ratio, and spectral resolution) on measurement error. There is also a lack of research aimed at developing ML algorithms specifically for HUM. Finally, there are emerging concepts such as sensor fusion and model-sensor fusion which have been applied to similar fields but are not common in studies involving HUM. This review suggests the need for further studies to better understand the factors that influence HUM measurement accuracy along with the need for hardware and software developments so that the methodology can ultimately become more robust and standardized. This, in turn, could increase its adoption in both academic and non-academic settings. Once the HUM methodology has matured, it could help to reduce the environmental impacts of society by improving our understanding and management of environmental systems through high-frequency data collection and automated control of water quality in environmentally relevant systems.

Experimental evidence that rill-bed morphology is governed by emergent nonlinear spatial dynamics.

Past experimental work found that rill erosion occurs mainly during rill formation in response to feedback between rill-flow hydraulics and rill-bed roughness, and that this feedback mechanism shapes rill beds into a succession of step-pool units that self-regulates sediment transport capacity of established rills. The search for clear regularities in the spatial distribution of step-pool units has been stymied by experimental rill-bed profiles exhibiting irregular fluctuating patterns of qualitative behavior. We hypothesized that the succession of step-pool units is governed by nonlinear-deterministic dynamics, which would explain observed irregular fluctuations. We tested this hypothesis with nonlinear time series analysis to reverse-engineer (reconstruct) state-space dynamics from fifteen experimental rill-bed profiles analyzed in previous work. Our results support this hypothesis for rill-bed profiles generated both in a controlled lab (flume) setting and in an in-situ hillside setting. The results provide experimental evidence that rill morphology is shaped endogenously by internal nonlinear hydrologic and soil processes rather than stochastically forced; and set a benchmark guiding specification and testing of new theoretical framings of rill-bed roughness in soil-erosion modeling. Finally, we applied echo state neural network machine learning to simulate reconstructed rill-bed dynamics so that morphological development could be forecasted out-of-sample.

Nitrogen-enriched discharges from a highly managed watershed intensify red tide (Karenia brevis) blooms in southwest Florida.

Karenia brevis blooms on Florida's Gulf Coast severely affect regional ecosystems, coastal economies, and public health, and formulating effective management and policy strategies to address these blooms requires an advanced understanding of the processes driving them. Recent research suggests that natural processes explain offshore bloom initiation and shoreward transport, while anthropogenic nutrient inputs may intensify blooms upon arrival along the coast. However, past correlation studies have failed to detect compelling evidence linking coastal blooms to watershed covariates indicative of anthropogenic inputs. We explain why correlation is neither necessary nor sufficient to demonstrate a causal relationship-i.e., a persistent pattern of interaction governed by deterministic rules-and pursue an empirical investigation leveraging the fact that systematic temporal patterns may reveal systematic cause-and-effect relationships. Using time series derived from in-situ sample data, we applied singular spectrum analysis-a non-parametric spectral decomposition method-to recover deterministic signals in the dynamics of K. brevis blooms and upstream water quality and discharge covariates in the Charlotte Harbor region between 2012 and 2021. Next, we applied causal analysis methods based on chaos theory-i.e., convergent cross-mapping and S-mapping-to detect and quantify persistent, state-dependent interaction regimes between coastal blooms and watershed covariates. We discovered that nitrogen-enriched Caloosahatchee River discharges have consistently intensified K. brevis blooms to varying degrees over time. River discharge was typically most influential at the earliest stages of blooms, while total nitrogen concentrations exerted the strongest influence during blooms' growth/maintenance stages. These results indicate that discharges and nitrogen inputs influence blooms through distinct yet synergistic causal mechanisms. Additionally, we traced this anthropogenic influence upstream to Lake Okeechobee (which discharges to the Caloosahatchee River) and the Kissimmee River basin (which drains into Lake Okeechobee), suggesting that watershed-scale nutrient management and modifications to Lake Okeechobee discharge protocols will likely be necessary to mitigate coastal blooms.

Reconstructing dynamics of foodborne disease outbreaks in the US cattle market from monitoring data.

Conventional empirical studies of foodborne-disease outbreaks (FDOs) in agricultural markets are linear-stochastic formulations hardwiring a world in which markets self-correct in response to external random shocks including FDOs. These formulations were unequipped to establish whether FDOs cause market reaction, or whether markets endogenously propagate outbreaks. We applied nonlinear time series analysis (NLTS) to reconstruct annual dynamics of FDOs in US cattle markets from CDC outbreak data, live cattle futures market prices, and USDA cattle inventories from 1967-2018, and used reconstructed dynamics to detect causality. Reconstructed deterministic nonlinear market dynamics are endogenously unstable-not self-correcting, and cattle inventories drive futures prices and FDOs attributed to beef in temporal patterns linked to a multi-decadal cattle cycle undetected in daily/weekly price movements investigated previously. Benchmarking real-world dynamics with NLTS offers more informative and credible empirical modeling at the convergence of natural and economic sciences.

Seasonal dynamics of terrestrially sourced nitrogen influenced Karenia brevis blooms off Florida's southern Gulf Coast.

Harmful algal blooms (HABs) threaten coastal ecological systems, public health, and local economies, but the complex physical, chemical, and biological processes that culminate in HABs vary by locale and are often poorly understood. Despite broad recognition that cultural eutrophication may exacerbate nearshore bloom events, the association is typically not linear and is often difficult to quantify. Off the Gulf Coast of Florida, Karenia brevis blooms initiate in the open waters of the Gulf of Mexico, and advection of cells supplies nearshore blooms. However, past work has struggled to describe the relationship between terrestrial nutrient runoff and bloom maintenance near the Gulf Coast. This study applied a novel nonlinear time series (NLTS) analytical framework to investigate whether nearshore bloom dynamics observed near Charlotte Harbor, FL were causally and systematically driven by terrestrially sourced inputs of nitrogen, phosphorus, and freshwater between 2012 and 2018. Singular spectrum analysis (SSA) isolated low-dimensional, deterministic signals in K. brevis log10-density dynamics and in the dynamics of nine of 10 candidate drivers. The predominantly seasonal K. brevis signal was strong, explaining 77.6% of the total variance in the observed time series. Causal tests with convergent cross-mapping provided evidence that nitrogen concentrations measured at the discharge point of the Caloosahatchee River systematically influenced K. brevis bloom dynamics. However, further causal testing failed to link these nitrogen dynamics to an upstream basin, possibly due to data limitations. The results support the hypothesis that anthropogenic nitrogen runoff facilitated the growth of K. brevis blooms near Charlotte Harbor and suggest that bloom events would be mitigated by nitrogen source and transport controls within the Caloosahatchee and/or Kissimmee River basins. More broadly, this work demonstrates that management-relevant causal inferences into the drivers of HABs may be drawn from available monitoring records.

Digital Proxy of a Bio-Reactor (DIYBOT) combines sensor data and data analytics to improve greywater treatment and wastewater management systems.

Technologies to treat wastewater in decentralized systems are critical for sustainable development. Bioreactors are suitable for low-energy removal of inorganic and organic compounds, particularly for non-potable applications where a small footprint is required. One of the main problems associated with bioreactor use is sporadic spikes of chemical toxins, including nanoparticles. Here, we describe the development of DIYBOT (Digital Proxy of a Bio-Reactor), which enables remote monitoring of bioreactors and uses the data to inform decisions related to systems management. To test DIYBOT, a household-scale membrane aerated bioreactor with real-time water quality sensors was used to treat household greywater simulant. After reaching steady-state, silver nanoparticles (AgNP) representative of the mixture found in laundry wastewater were injected into the system to represent a chemical contamination. Measurements of carbon metabolism, effluent water quality, biofilm sloughing rate, and microbial diversity were characterized after nanoparticle exposure. Real-time sensor data were analyzed to reconstruct phase-space dynamics and extrapolate a phenomenological digital proxy to evaluate system performance. The management implication of the stable-focus dynamics, reconstructed from observed data, is that the bioreactor self-corrects in response to contamination spikes at AgNP levels below 2.0 mg/L. DIYBOT may help reduce the frequency of human-in-the-loop corrective management actions for wastewater processing.

Reconstructing systematic persistent impacts of promotional marketing with empirical nonlinear dynamics.

An empirical question of long-standing interest is how price promotions affect a brand's sale shares in the fast-moving consumer-goods market. We investigated this question with concurrent promotions and sales records of specialty beer brands pooled over Tesco stores in the UK. Most brands were continuously promoted, rendering infeasible a conventional approach of establishing impact against an off-promotion sales baseline, and arguing in favor of a dynamics approach. Moreover, promotion/sales records were volatile without easily-discernable regularity. Past work conventionally attributed volatility to the impact of exogenous random shocks on stable markets, and reasoned that promotions have only an ephemeral impact on sales shares in stationary mean-reverting stochastic markets, or a persistent freely-wandering impact in nonstationary markets. We applied new empirical methods from the applied sciences to uncover an overlooked alternative: 'systematic persistence' in which promotional impacts evolve systematically in an endogenously-unstable market governed by deterministic-nonlinear dynamics. We reconstructed real-world market dynamics from the Tesco dataset, and detected deterministic-nonlinear market dynamics. We used reconstructed market dynamics to identify a complex network of systematic interactions between promotions and sales shares among competing brands, and quantified/characterized the dynamics of these interactions. For the majority of weeks in the study, we found that: (1) A brand's promotions drove down own sales shares (a possibility recognized in the literature), but 'cannibalized' sales shares of competing brands (perhaps explaining why brands were promoted despite a negative marginal impact on own sales shares); and (2) Competitive interactions between brands owned by the same multinational brewery differed from those with outside brands. In particular, brands owned by the same brewery enjoyed a 'mutually-beneficial' relationship in which an incremental increase in the sales share of one marginally increased the sales share of the other. Alternatively, the sales shares of brands owned by different breweries preyed on each other's market shares.

A nonlinear dynamics approach for incorporating wind-speed patterns into wind-power project evaluation.

Wind-energy production may be expanded beyond regions with high-average wind speeds (such as the Midwest U.S.A.) to sites with lower-average speeds (such as the Southeast U.S.A.) by locating favorable regional matches between natural wind-speed and energy-demand patterns. A critical component of wind-power evaluation is to incorporate wind-speed dynamics reflecting documented diurnal and seasonal behavioral patterns. Conventional probabilistic approaches remove patterns from wind-speed data. These patterns must be restored synthetically before they can be matched with energy-demand patterns. How to accurately restore wind-speed patterns is a vexing problem spurring an expanding line of papers. We propose a paradigm shift in wind power evaluation that employs signal-detection and nonlinear-dynamics techniques to empirically diagnose whether synthetic pattern restoration can be avoided altogether. If the complex behavior of observed wind-speed records is due to nonlinear, low-dimensional, and deterministic system dynamics, then nonlinear dynamics techniques can reconstruct wind-speed dynamics from observed wind-speed data without recourse to conventional probabilistic approaches. In the first study of its kind, we test a nonlinear dynamics approach in an application to Sugarland Wind-the first utility-scale wind project proposed in Florida, USA. We find empirical evidence of a low-dimensional and nonlinear wind-speed attractor characterized by strong temporal patterns that match up well with regular daily and seasonal electricity demand patterns.

Endogenously determined cycles: empirical evidence from livestock industries.

This paper applies the techniques of phase space reconstruction and recurrence quantification analysis to investigate U.S. livestock cycles in relation to recent literature on the business cycle. Results are presented for pork and cattle cycles, providing empirical evidence that the cycles themselves have slowly diminished. By comparing the evolution of production processes for the two livestock cycles we argue that the major cause for this moderation is largely endogenous. The analysis suggests that previous theoretical models relying solely on exogenous shocks to create cyclical patterns do not fully capture changes in system dynamics. Specifically, the biological constraint in livestock dynamics has become less significant while technology and information are relatively more significant. Concurrently, vertical integration of the supply chain may have improved inventory management, all resulting in a small, less deterministic, cyclical effect.

Tachycardia-induced early afterdepolarizations: insights into potential ionic mechanisms from computer simulations.

Although early afterdepolarizations (EADs) are classically thought to occur at slow heart rates, mounting evidence suggests that EADs may also occur at rapid heart rates produced by tachyarrhythmias, due to Ca overload of the sarcoplasmic reticulum (SR) leading to spontaneous SR Ca release. We hypothesized that the mechanism of tachycardia-induced EADs depends on the spatial and temporal morphology of spontaneous SR Ca release, and tested this hypothesis in computer simulations using a ventricular action potential mathematical model. Using two previously suggested spontaneous release morphologies, we found two distinct tachycardia-induced EAD mechanisms: one mechanistically similar to bradycardia-induced EADs, the other to delayed afterdepolarizations (DADs).

Effects of early afterdepolarizations on reentry in cardiac tissue: a simulation study.

Early afterdepolarizations (EADs) are classically generated at slow heart rates when repolarization reserve is reduced by genetic diseases or drugs. However, EADs may also occur at rapid heart rates if repolarization reserve is sufficiently reduced. In this setting, spontaneous diastolic sarcoplasmic reticulum (SR) Ca release can facilitate cellular EAD formation by augmenting inward currents during the action potential plateau, allowing reactivation of the window L-type Ca current to reverse repolarization. Here, we investigated the effects of spontaneous SR Ca release-induced EADs on reentrant wave propagation in simulated one-, two-, and three-dimensional homogeneous cardiac tissue using a version of the Luo-Rudy dynamic ventricular action potential model modified to increase the likelihood of these EADs. We found: 1) during reentry, nonuniformity in spontaneous SR Ca release related to subtle differences in excitation history throughout the tissue created adjacent regions with and without EADs. This allowed EADs to initiate new wavefronts propagating into repolarized tissue; 2) EAD-generated wavefronts could propagate in either the original or opposite direction, as a single new wave or two new waves, depending on the refractoriness of tissue bordering the EAD region; 3) by suddenly prolonging local refractoriness, EADs caused rapid rotor displacement, shifting the electrical axis; and 4) rapid rotor displacement promoted self-termination by collision with tissue borders, but persistent EADs could regenerate single or multiple focal excitations that reinitiated reentry. These findings may explain many features of Torsades des pointes, such as perpetuation by focal excitations, rapidly changing electrical axis, frequent self-termination, and occasional degeneration to fibrillation.

Intracellular calcium cycling, early afterdepolarizations, and reentry in simulated long QT syndrome.

OBJECTIVES: The purpose of this study was to investigate interactions between early afterdepolarizations (EADs) and reentry in long QT (LQT) syndromes. BACKGROUND: EADs, a characteristic feature of congenital and acquired LQT syndromes, are classically bradycardia dependent. Mechanisms by which they promote tachyarrhythmias such as torsades de pointes and ventricular fibrillation are not fully understood. Recent evidence suggests that EADs also may occur at rapid heart rates as a sequela of spontaneous sarcoplasmic reticulum (SR) Ca(2+) release related to intracellular Ca(2+) overload. Here, we performed computer simulations to explore the arrhythmogenic consequences of this phenomenon. METHODS: We used a modified version of the Luo-Rudy dynamic model in one-dimensional and two-dimensional cardiac tissue with the time-dependent K(+) currents I(Kr) or I(Ks) reduced by 50% to simulate acquired and congenital LQT syndromes. RESULTS: (1) Spontaneous SR Ca(2+) release prolonged action potential duration but did not induce overt EADs unless K(+) current density was reduced to simulate acquired and congenital LQT syndromes. (2) In simulated LQT syndromes, EADs were capable of both terminating and reinitiating one-dimensional reentry. (3) A similar phenomenon in simulated 2D tissue led to reinitiation of spiral wave reentry that otherwise would have self-terminated. (4) Reentry reinitiation occurred only when the L-type Ca(2+) current and SR Ca(i) cycling were potentiated to simulate moderate sympathetic stimulation, consistent with the known arrhythmogenic effects of sympathetic activation (and protection by beta-blockers) in LQT syndromes. CONCLUSIONS: These computer simulations suggest that EADs related to spontaneous SR Ca(2+) release can enhance arrhythmogenesis in LQT syndromes by reinitiating reentry.

Community dynamics in a university environment.

Scholars have characterized academic communities of faculty, administration, and students in U.S. universities as "organized anarchies." In contrast, we offer evidence that the community structures of two representative public university systems are notably systematic by applying empirical phase-diagram techniques from the nonlinear dynamics literature to reconstruct low-dimensional deterministic behavior from historic data on the coevolution of faculty, administration, and student populations in each system. Ecological community models, fit with population data for each university, reproduce the essence of this behavior. The models offer novel explanations of how university resources obtained from enrollments and other sources are systematically partitioned among faculty, administration, and student populations interacting in shifting and well-defined community roles. This work offers empirical evidence that ecological principles, typically reserved for characterizing nonhuman interactions in biological systems, can shed light on human interactions in social systems.

Controlling Forest Damage by Dispersive Beaver Populations: Centralized Optimal Management Strategy.

The beaver (Castor canadensis) population in the United States has caused severe damage to valuable timberland through dam-building and flooding of bottomland forest. Traditionally, beavers have provided a source of livelihood to a small group of people. However, recent low pelt prices have failed to stimulate adequate trapping pressure, and thus have resulted in increased beaver populations and damage losses. The low trapping pressure has left the burden of nuisance control on property owners. Since beaver populations are mobile, beaver extermination in controlled parcels results in beaver immigration from neighboring less controlled parcels. Beaver migration from less controlled to controlled parcels imposes an external cost (negative diffusion externality) on the owners of controlled parcels because they must incur the future cost of trapping immigrating beavers. Unless all land owners agree to control the beaver population simultaneously, the diffusion externality can decrease the incentive of individual landowners to control nuisance beavers, thereby driving a wedge between social and private needs for such control. This study attempts to develop a bioeconomic model that incorporates dispersive population dynamics of beavers into the design of a cost-minimizing trapping strategy. Attention is focused on the situation where all landowners in a given habitat share a common interest in controlling beaver damages, and thus collectively agree to place the area-wide control decision in the hands of a public agency on a cost-sharing basis. The public manager is assumed to minimize the present value of combined timber damage and trapping costs over a finite period of time, subject to spatiotemporal dynamics of beaver population. These dynamics are summarized by a parabolic diffusive Volterra-Lotka partial differential equation, and the population control problem is cast in the framework of a distributed-parameter-control model. The cost-minimizing area-wide trapping model accounts for net migration at each location and time, and characterizes the beaver-control strategy that leaves sufficient beavers to strike an optimal balance between timber damage and trapping costs. The marginality condition governing this trade-off requires that avoided timber damage (measured as the imputed nuisance value, or "shadow price," of the beaver stock in the area) be balanced by trapping cost. The optimality system for this problem is solved numerically. The validity of the theoretical model is empirically examined using the bioeconomic data collected for the Wildlife Management Regions of the New York State Department of Environmental Conservation. Empirical simulation generates discrete values for optimal beaver densities and trapping rates across all individual operational units over time. The optimal trapping program causes the initially uneven population distribution to eventually smooth out across the habitat. The sensitivity analysis alternates trapping-cost and timber-damage parameters between high and low values. Increased trapping costs decrease the level of trapping in the initial years of the optimal program, thereby leaving more beavers in the habitat. This triggers more intensive trapping during the later years of the program, requires more beavers to be trapped over the entire time horizon, and results in a higher overall program cost. Alternatively, increased timber-damage potential calls for increased trapping in the initial years of the program. Fewer beavers are maintained in the habitat and less trapping is required in the later years. Perhaps surprisingly, this results in a smaller number of beavers trapped over the entire time horizon.