Search Results (706)

Nitrogen (N) fertilizer management in winter wheat production faces challenges from volatilization losses and sub-optimal application strategies. This is particularly problematic in the Southern Great Plains, where environmental conditions during top-dressing periods favor N losses. This study evaluated the effects of a fertilizer placement method, enhanced-efficiency fertilizers, and application timing on grain yield and protein concentration (GPC) across six site-years in Oklahoma (2016–2018). Treatments included broadcast applications of untreated urea and SuperU^® (urease/nitrification inhibitor-treated urea). These were compared with subsurface placement using single-disc and double-disc drilling systems, applied at 67 kg N ha⁻¹ during January, February, or March. Subsurface placement increased the grain yield by 324–391 kg ha⁻¹ compared to broadcast applications at sites with favorable soil conditions. However, responses varied significantly across environments. Enhanced-efficiency fertilizers showed limited advantages over untreated urea. Benefits were most pronounced during February applications under conditions favoring volatilization losses. Application timing effects were more consistent for GPC than for the yield. Later applications (February–March) increased GPC by 0.8–1.2% compared to January applications. Treatment efficacy was strongly influenced by soil pH, equipment performance, and post-application environmental conditions. This indicates that N management benefits are highly site-specific. These findings demonstrate that subsurface placement can improve nitrogen use efficiency (NUE) under appropriate conditions. However, success depends on matching application strategies to local soil and environmental factors rather than adopting universal recommendations. Full article

The Guinea Pig X Virus (GPXV), a newly identified gammaherpesvirus, provides an opportunity to study viral evolution and host–virus dynamics. This study characterizes the GPXV genome and investigates its phylogenetic relationships and divergence from related viruses through comparative genomic and phylogenetic analyses. Virus propagation was conducted in Vero cells, followed by genomic DNA extraction and pan-herpesvirus nested PCR. Sanger sequencing filled gaps in the initial genome assembly, and whole-genome sequencing was performed using the Illumina MiSeq platform. Phylogenetic analyses focused on ORF8 (glycoprotein B), ORF9 (DNA polymerase catalytic subunit), ORF50 (RTA: replication and transcription activator), and ORF73 (LANA: latency-associated nuclear antigen). Results showed that GPXV ORFs showed variable evolutionary relationships with other gammaherpesviruses, including divergence from primate-associated viruses and clustering with bovine and rodent viruses. In addition to phylogenetics, a comprehensive comparative analysis of protein-coding genes between GPXV and the previously described Guinea Pig Herpes-Like Virus (GPHLV) revealed divergence. Twenty-four non-ORF genomic features were unique to GPXV, while 62 shared ORFs exhibited low to high sequence divergence. These findings highlight GPXV’s distinct evolutionary trajectory and its potential role as a model for studying host-specific adaptations and gammaherpesvirus diversity. Full article

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

While boreal forests constitute 30% of the Earth’s forested area and are responsible for 20% of the global carbon sink, there is considerable concern about their sustainability. This paper focuses on the role of elevated CO₂, examining whether wood volume in these forests has responded to increased CO₂ over the last 60 years. To accomplish this, we use a rich set of wood volume measurement data from the Province of Alberta, Canada, and deploy quasi-experimental techniques to determine the effect of elevated CO₂. While the few experimental studies that have examined boreal forests have found almost no effect of elevated CO₂, our results indicate that a 1.0% increase in lifetime exposure to CO₂ leads to a 1.1% increase in aboveground wood volume in these boreal forests. This study showcases the value of research designs that use natural settings to better account for the effects of prolonged exposure to elevated CO₂. Our results should enable improved delineation of the drivers of historical changes in wood volume and carbon storage in boreal forests. In addition, when combined with other studies, these results will likely aid policymakers in designing management or policy approaches that will enhance the sustainability of forests in boreal regions. Full article

Extracorporeal cardiopulmonary resuscitation (ECPR) is an established intervention for select patients experiencing refractory cardiac arrest. Among modifiable predictors of survival and neurologic recovery during ECPR implementation, timely restoration of circulation remains critical in the setting of refractory cardiac arrest (CA). The in-hospital cardiac arrest (IHCA) setting is particularly amenable to reducing the low-flow interval through structured system-based design and implementation. Despite increasing utilization of ECPR, the literature remains limited regarding operational standards, quality improvement metrics, and performance evaluation. Establishing operational standards and performance metrics is a critical first step toward systematically reducing low-flow interval duration. In support of this aim, we conducted a comprehensive literature review structured around the Extracorporeal Life Support Organization (ELSO) framework for ECPR implementation. At each step, we synthesized evidence-based best practices and identified operational factors that directly influence time-to-circulation. Our goal is to provide a stepwise evaluation of ECPR initiation to consolidate existing best practices and highlight process components with potential for further study and standardization. We further evaluated the literature surrounding key technical components of ECPR, including cannula selection, placement technique, and positioning. Ongoing research is needed to refine and standardize each stage of the ECPR workflow. Developing optimized, protocol-driven approaches to ensure rapid, high-quality deployment will be essential for improving outcomes with this lifesaving but resource-intensive therapy. Full article

Amid the growing debate over how cryptocurrencies are reshaping global finance, this study explores the nexus between Bitcoin, Brent Crude Oil, Gold and the U.S. Dollar Index. We used a time-varying vector autoregressive (tvVAR) model to examine the connection among these four assets during the Trump (2017–2020) and Biden (2021–2024) governments. The 48-week return forecast of the Bitcoin–Gold correlation was also conducted by using the Bayesian Structural Time Series (BSTS) model. Results indicate that Bitcoin was the most volatile asset, while the U.S. Dollar remained the least volatile under both regimes. Under Trump, U.S. Dollar significantly influenced Oil and Bitcoin while Bitcoin and Gold were negatively linked to Oil and positively associated with U.S. Dollar. An inverse relationship between Bitcoin and Gold also emerged. Under Biden, Bitcoin, Gold, and U.S. Dollar all significantly affected Oil with Bitcoin showing a positive impact. Bitcoin and Gold remained negatively correlated though not significantly, and the Dollar maintained positive ties with both. Forecasts show a positive link between Bitcoin and Gold in the coming year. However, Bitcoin does not exhibit consistent characteristics of a safe-haven asset during the U.S. presidential transitions examined, largely due to its high volatility and unstable correlations with a traditional safe-haven asset, Gold. This study contributes to the understanding of shifting relationships between digital and traditional assets across political regimes. Full article

Background/Objectives: Tuberculosis (TB) remains a major public health challenge in Davao City, Philippines, with persistent issues in both disease burden and treatment outcomes. Understanding the risk factors for TB and its unsuccessful treatment is essential for guiding effective interventions. This study aimed to evaluate the association of sociodemographic and clinical factors with TB occurrence and to identify predictors of unsuccessful TB treatment outcomes among patients in Davao City. Methods: A retrospective cohort study was conducted using data from 521 patients diagnosed with drug-susceptible TB at Davao Chest Center between January 2021 and May 2024. The sociodemographic and clinical profiles of the patients were described using descriptive statistics. Chi-square tests were used to assess the associations between sociodemographic and clinical variables with TB risk and treatment outcomes. Results: The patient cohort was predominantly aged 31–50 years (n = 201, 38.58%), male (n = 284, 54.51%), and married (n = 285, 54.70%), with most residing in Districts I and II (n = 98, 38% each), and had no previous TB treatment (n = 344, 66.03%). Among the 456 patients assessed for comorbidities, 56.14% (n = 256) had at least one comorbidity. Evaluation of the risk factors for TB occurrence among the study population revealed that comorbidity status was not significantly associated with an increased risk of TB diagnosis (p = 0.682). However, among patients diagnosed with TB, the presence of comorbidities was significantly associated with unsuccessful treatment outcomes (p = 0.003). Conclusions: Although sociodemographic factors did not significantly influence TB risk or treatment outcomes, the presence of comorbidities was a significant predictor of unsuccessful TB treatment. These findings highlight the importance of integrating comorbidity management with TB care to improve treatment success in high-burden urban settings. Full article

The phenomenon of rising forward price volatility, both historical and implied, as maturity approaches is referred to as the Samuelson effect or maturity effect. Disregarding this effect leads to significant mispricing of early-exercise options, extendible options, or other path-dependent options. The primary objective of the research is to identify a practical way to incorporate the Samuelson effect into the evaluation of commodity derivatives. We choose to model the instantaneous variance employing the exponential decay parameterizations of the Samuelson effect. We develop efficient calibration techniques utilizing historical futures data and conduct an analysis of statistical errors to provide a benchmark for model performance. The study employs 15 years of data for WTI, Brent, and NG, producing excellent results, with the fitting error consistently inside the statistical error, except for the 2020 crisis period. We assess the stability of the fitted parameters via cross-validation techniques and examine the model’s out-of-sample efficacy. The approach is generalized to encompass seasonal commodities, such as natural gas and electricity. We illustrate the application of the calibrated model of instantaneous variance for the evaluation of commodity derivatives, including swaptions, as well as in the evaluation of power purchase agreements (PPAs). We demonstrate a compelling application of the Samuelson effect to a widely utilized auto-callable equity derivative known as the snowball. Full article

Contrary to what is represented in geospatial databases, places are dynamic and shaped by events. Point clustering analysis commonly assumes events occur in an empty space and therefore ignores geospatial features where events take place. This research introduces relational density, a novel concept redefining density as relative to the spatial structure of geospatial features rather than an absolute measure. Building on this, we developed Space-Time Plume, a new algorithm for detecting and tracking evolving event clusters as smoke plumes in space and time, representing dynamic places. Unlike conventional density-based methods, Space-Time Plume dynamically adapts spatial reachability based on the underlying spatial structure and other zone-based parameters across multiple temporal intervals to capture hierarchical plume dynamics. The algorithm tracks plume progression, identifies spatiotemporal relationships, and reveals the emergence, evolution, and disappearance of event-driven places. A case study of crime events in Dallas, Texas, USA, demonstrates the algorithm’s performance and its capacity to represent and compute criminogenic places. We further enhance metaball rendering with Perlin noise to visualize plume structures and their spatiotemporal evolution. A comparative analysis with ST-DBSCAN shows Space-Time Plume’s competitive computational efficiency and ability to represent dynamic places with richer geographic insights. Full article

Bidirectionality between insomnia and depression is well documented in general and clinical populations but remains under-researched in family caregivers of people with dementia. This study aimed to explore this relationship using a longitudinal design with 155 family caregivers assessed annually over three years. Data collected included sociodemographic information, health behaviors, medical data, caregiving stressors, and depressive and insomnia symptoms. Two linear mixed models were tested: Model 1 considered insomnia symptoms as the independent variable and depressive symptoms as the outcome; Model 2 considered depressive symptoms as the independent variable and insomnia symptoms as the outcome. The results showed that caregivers with more insomnia symptoms over time had significantly higher depressive symptoms, even after adjusting for covariates. Insomnia accounted for an additional 7.47% of the variance, with a total explained variance of 57.93%. Conversely, higher depressive symptoms over time were associated with increased insomnia. Depressive symptoms explained an additional 7.28% of the variance, with a total explained variance of 25.74%. These results were consistent with previous studies on non-caregiving populations, adding empirical evidence to the notion that both insomnia and depression may operate as a risk factor for the other disorder. Caregiver support interventions could improve their psychological well-being if they incorporate sleep-focused strategies. Full article

Antimicrobial use in food animals selects for antimicrobial-resistant (AMR) bacteria, which most commonly reach humans via the food chain. However, AMR bacteria can also escape the feedyard via agricultural runoff, manure used as crop fertilizer, and even dust. A study published in 2015 reported AMR genes in dust from cattle feedyards; however, one of the study’s major limitations was the failure to investigate gene presence in viable bacteria, or more importantly, viable bacteria of importance to human health. Our main objective was to investigate the presence and quantity of viable bacteria and antimicrobial-resistant (AMR) determinants in fugitive bioaerosols from cattle feedyards in the downwind environment. Six bioaerosol sampling campaigns were conducted at three commercial beef cattle feedyards to assess variability in viable bacteria and AMR determinants associated with geographic location, meteorological conditions, and season. Dust samples were collected using four different sampling methods, and spiral plated in triplicate on both non-selective and antibiotic-selective media. Colonies of total aerobic bacteria, Enterococcus spp., Salmonella enterica, and Escherichia coli were enumerated. Viable bacteria, including AMR bacteria, were identified in dust from cattle feedyards. Bacteria and antimicrobial resistance genes (ARGs via qPCR) were mainly found in downwind samples. Total suspended particles (TSPs) and impinger samples yielded the highest bacterial counts. Genes encoding beta-lactam resistance (bla_CMY-2 and bla_CTX-M) were detected while the most common ARG was tet(M). The predominant Salmonella serovar identified was Lubbock. Further research is needed to assess how far viable AMR bacteria can travel in the ambient environment downwind from cattle feedyards, to model potential public health risks. Full article

Water distribution systems (WDSs) exhibit intricate, nonlinear behaviors shaped by both internal dynamics and external influences. The incorporation of additional models, such as contamination or population models, further increases their complexity. This study investigated WDSs under various uncertainty scenarios to enhance system stability, robustness, and control. In particular, we built upon prior research by exploring an Agent-Based Modeling (ABM) framework integrated within a WDS, focusing on three types of uncertainties: (1) adjustments to existing probabilistic parameters, (2) variations in agent movement across network nodes, and (3) changes in agent distributions across different node types. We conducted our analysis using the virtual city of Micropolis as a testbed. Our findings indicate that while the system remains resilient to uncertainties in predefined probabilistic parameters, substantial and often nonlinear effects arise when uncertainties are introduced in agent mobility and distribution patterns. These results emphasize the significance of understanding how WDSs respond to external behavioral dynamics, which is essential for managing real-world challenges, such as pandemics or shifts in urban behavior. This study underscores the necessity for further research into broader uncertainty categories and emergent effects to enhance WDS modeling and inform decision-making. Full article

Using the OLS model with different quantiles of GPR, we aim to examine the impact of GPR, EPU, and VIX on monthly international crude oil prices, including WTI, BRENT, and DUBAI prices, while differentiating the impact on different levels of risks. Afterwards, we use the GARCH and MGARCH models to assess the impact of these metrics on the volatility of oil prices, and the spillover effects between oil prices with these three metrics as exogenous shocks. Our result indicates (i) global oil price is negatively affected by GPRT at a moderate level of risks in longer time intervals; (ii) GPR, EPU, and VIX affect oil price’s volatility, and (iii) there exists a stronger long-persistent spillover effect between BRENT and DUBAI, with these metrics as exogenous shocks, while WTI is not affected. Full article

This paper examines the relationship between dependence and independence alternatives in general stochastic processes and explores the duality between the true (yet unknown) stochastic process and the functional representation that fits the observed data. We demonstrate that the solution depends on its historic realizations, challenging existing theoretical frameworks that assume independence between the solution and the history of the true process. Under orthogonality conditions, we investigate parameter spaces within data-generating processes and establish conditions under which data exhibit mean-reverting, random, cyclical, history-dependent, or explosive behaviors. We validate our theoretical framework through empirical analysis of an extensive dataset comprising daily prices from the S&P500, 10-year US Treasury bonds, the EUR/USD exchange rate, Brent oil, and Bitcoin from 1 January 2002 to 1 February 2024. Our out-of-sample predictions, covering the period from 17 February 2019 to 1 February 2024, demonstrate the model’s exceptional forecasting capability, yielding correct predictions with between 73% and 92% accuracy, significantly outperforming naïve and moving average models, which only achieved 47% to 54% accuracy. Full article

Since the 2018 Farm Bill legalized hemp, semi-synthetic cannabinoids, typically derived from hemp-extracted CBD, have been marketed as offering a “legal high”, raising concerns about consumer safety, labeling, and regulation. Consequently, the potency analysis of these compounds has become increasingly important. To address this need, an LC-DAD method was developed for the quantification of seventeen cannabinoids, selected based on the synthetic pathways of semi-synthetic cannabinoids. These included naturally occurring compounds, semi-synthetic derivatives, and byproducts (CBC, CBD, CBDV, CBG, CBN, CBN-O-acetate, CBT, 9(R)-HHC, 9(S)-HHC, 9(R)-HHC-O-acetate, 9(S)-HHC-O-acetate, Δ⁸-THC, Δ⁹-THC, Δ^9,11-THC, Δ⁸-THC-O-acetate, Δ⁹-THC-O-acetate, and Δ⁹-THCV), using abnormal CBD as an internal standard. The method was validated according to ISO 17025 guidelines, demonstrating a linear calibration range from 0.1 to 50 µg/mL. The method was further applied to the potency analysis of one Δ⁸-THC, two THC-O-acetate, two HHC, and one HHC-O-acetate vaping oil sample. Using an innovative method to recover the contents of vaping cartridges, cannabinoids were extracted using methanol, diluted to a concentration of 50 µg/mL, and analyzed using the validated LC-DAD method, which provided a quantifiable range of 0.1 to 100% (w/w). Method specificity was evaluated using ESI/TOFMS and showed minimal interference, despite the presence of other isomers of the semi-synthetic cannabinoids in the samples. Full article