Global mapping of pharmaceutical ecological risk in rivers using machine learning: drivers, hotspots, and compounded water stress.

ABSTRACT The Commonwealth Caribbean has a history of being ‘governed from outside’. While colonisation is usually associated with European metropoles, this paper argues that the Caribbean anti-trafficking in persons field has been subject to the US government as a neo-coloniser. This exogenous influence harms more than it helps. The article shows how vulnerable the region is to US dictates through financing and soft power. To solidify this argument, the work applies post-colonial discourse analysis to empirical fieldwork and anti-trafficking campaigns. The paper concludes that the region must extricate itself from this dependency using a more Caribbean-centric approach to address trafficking in persons.

We examined the effectiveness of machine-learning-based electrical demand forecasting frameworks in supporting short-term operational planning for power generation facilities. To this end, a forecasting workflow was designed that integrates statistical learning methods with deep neural architectures to capture both temporal demand dynamics and exogenous weather influences. Model performance was assessed under controlled experimental conditions using multiple accuracy metrics, alongside sensitivity analyses to evaluate the influence of engineered features on predictive stability. The system employed a coordinated multi-model training approach, incorporating temporal decomposition, contextual feature construction, and climate-aware inputs to improve robustness across varying load profiles. Component-level ablation experiments were conducted to isolate the contribution of individual architectural and feature-engineering elements to overall forecasting accuracy. Results indicate that precise short-term load estimation extends beyond historical consumption modeling; it enables more efficient fuel scheduling, supports grid reliability, and enhances the system’s capacity to respond to real-time demand fluctuations.

Previous studies have reported a higher rate of small for gestational age (SGA) newborns from fresh embryo transfers compared with frozen-thawed embryo transfers (FET), potentially due to the supraphysiologic serum estradiol levels observed during controlled ovarian stimulation. This study aimed to evaluate whether different regimens of exogenous estradiol administration influence live birth rates and neonatal outcomes in hormone replacement therapy (HRT)-FET cycles. We conducted a retrospective analysis of patients undergoing their first FET with HRT-based endometrial preparation between January 2015 and December 2018 at our center, comparing those who received estradiol both orally and vaginally (OVE group) with those who received estradiol orally only (OE group). Patients in the OVE group achieved higher serum estradiol levels and underwent longer durations of estradiol treatment. No significant differences were found in live birth rate (adjusted odds ratio [OR], 1.327; 95% confidence interval [CI], 0.982 to 1.794; p=0.066) or clinical pregnancy rate (adjusted OR, 1.278; 95% CI, 0.937 to 1.743; p=0.121). The estradiol regimen did not affect singleton birth weight (β=-30.962; standard error=68.723; p=0.653), the odds of large for gestational age (adjusted OR, 1.165; 95% CI, 0.545 to 2.490; p=0.694), the odds of SGA (adjusted OR, 0.569; 95% CI, 0.096 to 3.369; p=0.535), or preterm delivery (adjusted OR, 0.969; 95% CI, 0.292 to 3.214; p=0.959). Combined oral and In subsequent cycles, patients received estrogen administration did not alter live birth rates or singleton neonatal outcomes, but was associated with higher serum estradiol levels and potential maternal risks.

Urban climate action planning case studies: A review of trends, approaches, and reported barriers and drivers.

Evidence is accumulating regarding an association between autism and functional neurological disorder, a common cause for a wide range of neurological symptoms affecting motor, sensory and cognitive systems. Symptoms can include paralysis, tremors, sensory disturbance, vision loss and dizziness. Functional neurological disorder exists at the complex intersection of physical and mental health, neurology and psychiatry, and body and mind. Despite a recent resurgence in clinical and scientific interest, functional neurological disorder has lagged behind other causes of neurological symptoms in research, service development and acceptance. The nature of the association between autism and functional neurological disorder remains uncertain, but several plausible mechanisms can be identified from overlapping areas of research, highlighting endogenous factors such as atypical interoception, motor function, emotional processing and sensorimotor integration, alongside exogenous influences including adversity, healthcare inequality and stigma. This review first provides an overview of functional neurological disorder through various explanatory frameworks before applying biopsychosocial, neuropsychological and computational perspectives to conceptualise its intersection with autism. It then considers how this association might be understood and explores how services could be adapted to better recognise and support autistic individuals with functional neurological disorder across the diagnostic and treatment pathway.Lay AbstractFunctional neurological disorder causes real and often disabling symptoms, such as seizures, paralysis, tremors or sensory changes, even though standard medical tests do not show physical damage to the nervous system. Research suggests that autistic people are more likely to experience functional neurological disorder than their non-autistic peers, but the reasons for this are not yet understood. This article explores why autism and functional neurological disorder might occur together. It draws on research into how the brain processes body signals (like pain or movement), handles emotions and responds to uncertainty. It also looks at life experiences that affect health, including trauma, barriers to healthcare and stigma. This article shows that both internal factors (such as differences in movement, emotional awareness and sensory processing) and external factors (such as stress, inequality and misdiagnosis) may increase the chances of functional neurological disorder in some autistic individuals. Several models are introduced to help explain how these influences might interact. Finally, this article outlines how healthcare services could better support autistic people with functional neurological disorder. It encourages functional neurological disorder services to adapt communication styles, provide appropriate adjustments and include autistic voices in research and treatment planning to improve care and outcomes.

Accurate stream flow forecasting is essential for flood risk management and preparedness. This study compares two forecasting approaches: (a) the Seasonal Auto-Regressive Integrated Moving Average with Exogenous Regressors (SARIMAX), a classical statistical model, and (b) Prophet, a decomposable time-series forecasting model that incorporates seasonality and exogenous predictors. Forecasts were generated for 15-day and 3-day horizons and evaluated using uncertainty bounds, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R2). Results indicate that SARIMAX was less effective at capturing the observed variability, producing wide uncertainty (177.7%) and high errors (MAE = 153.73; RMSE = 207.10) with a negative R2 (–4.42). At shorter horizons, its performance remained limited (uncertainty = 28.04%; MAE = 61.52; RMSE = 94.88; R2 = –0.14). In contrast, Prophet achieved significantly lower uncertainty (16%), high accuracy (R2 = 0.95), and exceptional performance on short-term forecasts (R2 = 0.99). Conventional procedures such as SARIMAX have long been relied upon by engineers for their interpretability, and remain important as part of a strategy; however, they fail to represent nonlinear dynamics and exogenous influences now captured effectively by AI-based models. These findings highlight Prophet’s superiority across horizons and its promise for enhancing operational flood forecasting through its ability to effectively capture non-linear dynamics and exogenous influences.

In this article, a range based volatility model that integrated robust hybrid range-based estimators with exogenous variables was proposed for oil and gas sector. The study employed historical daily data on opening, closing, high, and low prices of stocks from Chevron, Conoil, Oando Plc, and Total Energies, spanning 1st January, 2012 to 12th September, 2025, alongside daily Brent crude oil prices as an exogenous variable. The model and exogenous influences on volatility was examined. The study showed that Range-Based Generalised Autoregressive Conditional Heteroskedasticity with Exogenous variable (RB-GARCH-X) models, especially the non-zero drift version, captured volatility in Nigeria’s oil and gas sector effectively, while the adaptive RB-GARCH-X model that adapt to both drift-free and drift-present conditions performed best for highly volatile stocks. The results also revealed that range-based estimation techniques improved model robustness against microstructure noise and estimation errors in both drift-free and drift-present conditions. Parameter estimates indicated strong volatility persistence (β: 0.71–0.99), moderate short-term effects (α: 0.03–0.21), and significant influence of crude oil prices (γ: ≈2.0E-07 to -1.25E-07). The adaptive model provided a more effective balance between short- and long-term effects, demonstrating its robustness for stocks characterised by heightened instability like the Nigeria Oil and Gas stock.

Exogenous influences on long-term inflation expectation deviations: Evidence from Chile

ABSTRACT Extensive wastewater discharge has significantly affected Lake Tanganyika's water quality (WQ), environment, and ecological integrity. Data was collected from 1998 to 2024 on the Buterere wastewater treatment plants, which gather all the untreated discharge of Bujumbura and industrial waste and release it into Lake Tanganyika. The wastewater discharge contained nonlinear factors, exogenous influences like stormwater, and irregular temporal patterns, making accurate predictions vital for adequate environmental protection, wastewater treatment, and resource management. To overcome these challenges, this study introduced an ensemble ARIMA-LSTM attention model for improving forecast accuracy. The ARIMA model captures linear parts, outputting nonlinear residuals, which the LSTM, enhanced by the attention mechanism, predicts. The proposed ensemble ARIMA-LSTM attention model surpassed Hybrid ARIMA-LSTM, LSTM, and ARIMA across all metrics. It achieves an MSE of 8,831.235 and MAE of 51.232 alongside MAPE of 1.002% with RMSLE of 0.009, RMSE of 38.681, Pbias of −0.024%, and R2 of 0.995. Slightly negative Pbias indicates a minor underestimation bias, desirable in environmental contexts, preventing overwhelming treatment facilities. This hybrid model, sporting an attention mechanism, substantially enhances forecasting accuracy for wastewater discharge. It enables timely WQ and ecological interventions, supporting decision-makers with essential data-driven insights for sustainable management and preservation of the ecosystem.

The microbiome plays an essential role in the development of the immune system. Both the immune system and microbiome dynamically respond to internal and external cues, and dysregulation of either of these systems can lead to disease pathology. Separate from the adaptive immune system, the innate immune system retains a memory of inflammatory events that determine the quality of future immune responses. The phenomenon is characterized by epigenetic modifications that lead to immunosuppressive or hyperinflammatory cell phenotypes, collectively designated as epigenetic cellular memory. It remains unclear whether and how the microbiome influences epigenetic cellular memory phenotypes to promote immunopathology and chronic disease. Inflammatory signals from the microbiota regulate hematopoiesis and systemic immunity through the production of immunomodulatory ligands and activation of circulating immune cells; however, few studies have directly implicated these mechanisms in the development of epigenetic cellular memory. We posit that a multi-omic systems approach is well-suited to elucidating the complex factors mediating the microbiome's contribution to this phenomenon. By measuring responses to exogenous influences through multi-omic technologies, it will be possible to identify the regulatory axis that next-generation therapies should target to reverse immunopathology. As chronic inflammatory disorders are on the rise, it is imperative that future therapies leverage both dietary and pharmacological interventions to promote self-reinforcing homeostatic immunity by targeting the mechanisms of epigenetic cellular memory.

Only a few would doubt the impacts of United States’ financial investments in Europe after World War II. The same cannot be said of its commitments to the countries of the Global South, whose buffeting challenges are compounded by livelihood-threatening proliferation of extremist non-state actors occasioned by, among other things, multidimensional deprivations. This study examines the exogenous influences and endogenous dynamics that contributed to the shaping of an agency purportedly established to coordinate American civilian aid to the developing world, the United States Agency for International Development (USAID), into an enabling conduit that funnelled American funds to extremist sects in affected countries. It addresses the question of whether USAID’s terror financing is a mitigating remedy or a style. Data is sourced through ‘process tracing approach’ and subjected to discourse analysis. The findings affirm that violent extremism financing is part of the flow of world events. They show there are instances that suggest that USAID financing of terror cells took place in the post-1998 period, but without a legislated or legal system. The study concludes that terrorism might be a backward vision of society orchestrated by frustration, but its financing by USAID is an intentional, breathtakingly superior weapon of political manipulation.

Wara Liang is a shoreline rockshelter on Lembata island, Indonesia, where excavation in 2017 revealed a deep stratigraphy preserving evidence of forager habitation from ca. 1200 years ago. At around 600 BP, the nature of the occupation changes with a range of new zooarchaeological remains appearing, including domesticated animals as well as a substantial assemblage of earthenware pottery with some exotic tradeware. The deposition of the Wara Liang pottery at this time seemingly represents a strikingly late arrival of pottery technology at this site. Here we discuss the Wara Liang ceramics assemblage and consider a range of scenarios that may account for this apparent late technology transfer. The historical context of the time and the intensification of exogenous contact and influence in Nusa Tenggara Timor, along with the essential environmental nature of the region with its history of natural disasters and displacement of populations, are discussed in terms of effects on local communities. We also highlight the oral history and origin legend of Lamalera, a village close by the Wara Liang rockshelter and famous for its tradition of hunting whales. This origin legend intriguingly sheds light on the first use of pottery in the Wara Liang locale and provides information that credibly supplements the pottery record.

At the beginning of July 2025, the global cryptocurrency market capitalisation reached more than $2.8 trillion, with 1 Bitcoin exchanging for more than $105,000. As cryptocurrencies are becoming part of the global financial infrastructure, monitoring their evolution is crucial for determining whether they can be considered a sustainable long-term financial exchange system. In this paper, we have reconstructed the network structures and dynamics of Bitcoin from its launch in January 2009 to December 2023 and identified its key evolutionary phases. Our results show that network centralisation and wealth concentration increased from the very early years, following a richer-get-richer mechanism. This trend was endogenous to the system, beyond any subsequent institutional or exogenous influence. The evolution of Bitcoin is characterised by three periods, Exploration, Adaptation, and Maturity, with substantial coherent network patterns. Our findings suggest that Bitcoin is a highly centralised structure, with high levels of wealth inequality and internally crystallised power dynamics, which may have negative implications for its long-term sustainability.

At the beginning of July 2025, the global cryptocurrency market capitalisation reached more than $2.8 trillion, with 1 Bitcoin exchanging for more than $105,000. As cryptocurrencies are becoming part of the global financial infrastructure, monitoring their evolution is crucial for determining whether they can be considered a sustainable long-term financial exchange system. In this paper, we have reconstructed the network structures and dynamics of Bitcoin from its launch in January 2009 to December 2023 and identified its key evolutionary phases. Our results show that network centralisation and wealth concentration increased from the very early years, following a richer-get-richer mechanism. This trend was endogenous to the system, beyond any subsequent institutional or exogenous influence. The evolution of Bitcoin is characterised by three periods, Exploration, Adaptation, and Maturity, with substantial coherent network patterns. Our findings suggest that Bitcoin is a highly centralised structure, with high levels of wealth inequality and internally crystallised power dynamics, which may have negative implications for its long-term sustainability.

In intensive industrial applications, such as cement manufacturing, the reliable operation of key equipment is critical to ensure the equipment longevity and a continuous efficient production. This study focuses on forecasting the mechanical vibrations in an Induced Draft (ID) fan used in a cement mill, where abnormal vibration levels may indicate impending faults or performance degradation. Given the dynamic and nonlinear behavior of such systems, accurate prediction is both challenging and essential for condition-based maintenance. A hybrid forecasting framework is utilized in the current study, that integrates the statistical accuracy of the Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX) model with the nonlinear learning capabilities of Long Short-Term Memory (LSTM) networks. The ARIMAX component captures the linear structure and exogenous influences, while the LSTM models capture the residual nonlinearities providing a holistic approach to the vibration behavior. The proposed hybrid model is benchmarked against three standalone approaches: ARIMAX, LSTM, and the Machine Learning (ML)-based XGBoost algorithm. The experimental results demonstrate that the hybrid ARIMAX–LSTM model significantly outperforms individual models in terms of prediction accuracy, as measured by the RMSE, MAE, and R² statistical metrics. These findings highlight the potential of combining classical time series models with Deep Learning (DL) architectures for advanced prognostics in industrial rotating machinery.

Disruption of circadian rhythms by abnormal light exposure and reduced melatonin secretion has been linked to heightened pain sensitivity and opioid tolerance. This study evaluated how environmental light manipulation and exogenous melatonin supplementation influence pain perception and morphine tolerance in a rat model of neuropathic pain induced by partial sciatic nerve transection (PSNT). Rats were exposed to constant darkness, constant light, or a 12 h/12 h light-dark cycle for one week before PSNT surgery. Behavioral assays and continuous intrathecal (i.t.) infusion of morphine, melatonin, or their combination were conducted over a 7-day period beginning immediately after PSNT. On Day 7, after discontinued drugs infusion, an acute intrathecal morphine challenge (15 µg, i.t.) was administered to assess tolerance expression. Constant light suppressed melatonin levels, exacerbated pain behaviors, and accelerated morphine tolerance. In contrast, circadian-aligned lighting preserved melatonin rhythms and mitigated these effects. Melatonin co-infusion attenuated morphine tolerance and enhanced morphine analgesia. Reduced pro-inflammatory cytokine expression and increase anti-inflammatory cytokine IL-10 level and suppressed astrocyte activation were also observed by melatonin co-infusion during morphine tolerance induction. These findings highlight the potential of melatonin and circadian regulation in improving opioid efficacy and reduced morphine tolerance in managing neuropathic pain.

Low-potential and coreactant-free electrochemiluminescence (ECL) is a promising approach to eliminate exogenous influences from interfering substances and coreactants. Herein, by exploiting the prestored-electron nature of n-type nanoparticles, low-potential and coreactant-free ECL was developed using 4-amino-2-(methylthio)pyrimidine-5-carboxylic acid (AMPC)-capped AuAg nanoclusters, i.e., AMPC-AuAgNCs (AuAgNCs) as luminophores. AuAgNCs prestored with endogenous conduction band (CB) electrons can inject exogenous valence band (VB) holes around +0.80 V (vs Ag/AgCl) through an electrochemical oxidation process. The accumulated ECL spectral data demonstrate that these VB holes were captured by traps around the VB of AuAgNCs. Then the captured trap holes can directly recombine with the endogenous CB electrons. Thus, a distinct coreactant-free ECL process was generated around +0.80 V in the absence of any exogenous coreactants. Importantly, AuAgNCs were utilized as ECL tags to perform sandwich-type sensitive and selective immunoassays, which exhibited a wide linear response for determining human prostate-specific antigen (PSA) from 0.5 pg/mL to 1000 pg/mL and a limit of detection of 0.1 pg/mL (S/N = 3). This biosensor efficiently eliminated systematic errors and enhanced detection reliability without the involvement of exogenous coreactants. It displayed good assay performance for human serum samples, holding great promise in biomedical research.

Malaria remains hyperendemic in Kenya’s Lake Victoria basin despite scalable interventions. It is a pressing public health challenge in Migori county that reported a 27% mortality rate in 2020 in children aged 6-59 months, far exceeding national levels. Reports indicate different contributing factors to malaria dynamics in Migori County, including marginal insecticide-treated net (ITN) use, ITN access, effective anti-malaria treatment, and prevalence of malaria infection. The present study seeks to elucidate the temporal interaction between malaria incidence and mortality by employing a range of time series analyses, incorporating exogenous influences by applying classical vector autoregressive (VAR) model to capture lagged dependencies. Further, the study invoked a Bayesian VAR (BVAR) after incorporating exogenous variables for parameter estimating, utilizing Monte Carlo simulations and Gibbs sampling. For model adequacy and forecast accuracy, the analysis made use of Ljung-Box test, partial autocorrelation function, autocorrelation function (ACF), and normality tests among other diagnostic tests. The hierarchical Bayesian vector autoregressive model (BVARX) incorporates monthly incidence and mortality rates (2014-2024, n=120) as the endogenous variables. The exogenous variables comprised ITN access and use, treatment efficacy, and infection prevalence. Ward-level heterogeneity summed the spatial random effects. Hamilton Monte Carlo model estimation with convergence assessed using R ̂<1.01 Counterfactual simulations quantified intervention impacts. ITN use reduced incidence (β = −1.43, 95% CrI: −2.21, −0.65) but access increased mortality (β = 1.81, CrI: 0.32, 3.30), suggesting behavioral misuse. VARX outperformed VAR (WAIC: 412 vs. 587), yet residual spatial autocorrelation (Moran’s I = 0.34, *p* = 0.01) indicated unobserved confounders. BVARX forecasts predicted 22% (CrI: 18–27%) higher incidence by 2025 under current interventions. The regression analysis identified that higher ITN use is significantly associated with reductions in both malaria mortality and incidence. While ITNs and treatments show efficacy, their benefits are eroded by suboptimal utilization and ecological feedback. The study recommended the use of ward-level VARX outputs for geospatial targeting of ITN campaigns as well as integrated resistance monitoring through adaptive Bayesian frameworks.

Malaria remains hyperendemic in Kenya’s Lake Victoria basin despite scalable interventions. It is a pressing public health challenge in Migori county that reported a 27% mortality rate in 2020 in children aged 6-59 months, far exceeding national levels. Reports indicate different contributing factors to malaria dynamics in Migori County, including marginal insecticide-treated net (ITN) use, ITN access, effective anti-malaria treatment, and prevalence of malaria infection. The present study seeks to elucidate the temporal interaction between malaria incidence and mortality by employing a range of time series analyses, incorporating exogenous influences by applying classical vector autoregressive (VAR) model to capture lagged dependencies. Further, the study invoked a Bayesian VAR (BVAR) after incorporating exogenous variables for parameter estimating, utilizing Monte Carlo simulations and Gibbs sampling. For model adequacy and forecast accuracy, the analysis made use of Ljung-Box test, partial autocorrelation function, autocorrelation function (ACF), and normality tests among other diagnostic tests. The hierarchical Bayesian vector autoregressive model (BVARX) incorporates monthly incidence and mortality rates (2014-2024, n=120) as the endogenous variables. The exogenous variables comprised ITN access and use, treatment efficacy, and infection prevalence. Ward-level heterogeneity summed the spatial random effects. Hamilton Monte Carlo model estimation with convergence assessed using R ̂<1.01 Counterfactual simulations quantified intervention impacts. ITN use reduced incidence (β = −1.43, 95% CrI: −2.21, −0.65) but access increased mortality (β = 1.81, CrI: 0.32, 3.30), suggesting behavioral misuse. VARX outperformed VAR (WAIC: 412 vs. 587), yet residual spatial autocorrelation (Moran’s I = 0.34, *p* = 0.01) indicated unobserved confounders. BVARX forecasts predicted 22% (CrI: 18–27%) higher incidence by 2025 under current interventions. The regression analysis identified that higher ITN use is significantly associated with reductions in both malaria mortality and incidence. While ITNs and treatments show efficacy, their benefits are eroded by suboptimal utilization and ecological feedback. The study recommended the use of ward-level VARX outputs for geospatial targeting of ITN campaigns as well as integrated resistance monitoring through adaptive Bayesian frameworks.