Uncertainty Range Research Articles

Task space control of the robot manipulators with adaptive fuzzy global fast terminal sliding mode control in presence of dynamic and kinematic uncertainties

SummaryBasic features such as convergence time and speed, number‐action control coefficients, free chattering, and proof of stability are significant in the design process and sliding mode control (SMC) efficiency. In this article, we propose an adaptive fuzzy global fast terminal SMC (AFGFTSMC) to handle the mentioned features in the task space control of the robot manipulator in the presence of dynamic and kinematic uncertainties. First, perturbed joint space dynamic equations of the system are transferred to task space, and a broad range of uncertainties are considered there. Then, a global fast terminal SMC (GFTSMC) is proposed for robot manipulators in task space, in which a flexible sliding surface improves the convergence time. Next, to have an intelligent adjustment of the sliding surface coefficients, which leads to a much faster convergence rate, a fuzzy approximator with just seven fuzzy rules is presented. In the following, to access the boundaries of the existing uncertainties, an adaptive fuzzy approximator is proposed, which has five fuzzy rules and only one adaptive law, increases the system's robustness, and eliminates the effect of chattering. Mathematical proof shows that the task space closed‐loop control system under the proposed AFGFTSMC and in the presence of dynamic and kinematic uncertainties has a finite‐time global asymptotic stability. The theoretical evidence and simulation results, which are conducted on a 2‐link robot manipulator, confirm the good efficiency of the proposed controller.

Shoot-through proton FLASH irradiation lowers linear energy transfer in organs at risk for neurological tumors and is robust against density variations

Objective. The goal of the study was to test the hypothesis that shoot-through FLASH proton beams would lead to lower dose-averaged LET (LETD) values in critical organs, while providing at least equal normal tissue sparing as clinical proton therapy plans. Approach. For five neurological tumor patients, pencil beam scanning (PBS) shoot-through plans were made, using the maximum energy of 227 MeV and assuming a hypothetical FLASH protective factor (FPF) of 1.5. The effect of different FPF ranging from 1.2 to 1.8 on the clinical goals were also considered. LETD was calculated for the clinical plan and the shoot-through plan, applying a 2 Gy total dose threshold (RayStation 8 A/9B and 9A-IonRPG). Robust evaluation was performed considering density uncertainty (±3% throughout entire volume). Main results. Clinical plans showed large LETD variations compared to shoot-through plans and the maximum LETD in OAR is 1.2–8 times lower for the latter. Although less conformal, shoot-through plans met the same clinical goals as the clinical plans, for FLASH protection factors above 1.4. The FLASH shoot-through plans were more robust to density uncertainties with a maximum OAR D2% increase of 0.6 Gy versus 5.7 Gy in the clinical plans. Significance. Shoot-through proton FLASH beams avoid uncertainties in LETD distributions and proton range, provide adequate target coverage, meet planning constraints and are robust to density variations.

Integrated framework for hydrologic modelling in data-sparse watersheds and climate change impact on projected green and blue water sustainability

Climate and hydrologic hazards pose a threat to the distribution of watersheds’ water resources in time and space, necessitating planning for sustainable resilience and adaptation. Hydrologic modelling has emerged as a potential solution for understanding watershed responses to projected climate change, and a prediction model that can deliver actionable information is necessary, although it requires basin-scale observations to calibrate the model to reliably predict basin-scale water resources hazards. Such luxury is not always tenable in watersheds with inadequate ground-based observation. However, satellite-based evapotranspiration (ET) data coupled with a machine learning feature selection as a data refinement process has made integrated water balance modelling widely regarded as a viable alternative for improving the capability of watershed modelling processes in data-sparse regions. This study developed a convincing hydrologic model framework to sufficiently calibrate and provide accurate behavioural solutions for all model responses. The framework was applied to four sub-basins that form the larger Lake Chad basin. The model results were applied to assess the dynamic changes in projected blue and green water resource sustainability in response to climate change in one of the sub-basins. Study findings indicate that hydrologic fluxes can be simulated accurately with varying degrees of acceptability, with R2 and NSE values in the range of 0.69–0.88 and 0.45–0.77 for calibration and 0.69–0.79 and 0.34–0.63 for validation, respectively, and captured within a satisfactory uncertainty range of P-factor and R-factor values of 0.68–0.93 and 0.73–1.31, respectively, in 83%, 67%, 85.7%, and 81.3% of the sub-watersheds based on multi-site simulation despite distinct watershed morphology, although there are significant trade-offs in parameter sensitivity. Whilst green water is the dominant freshwater component across the basin relative to blue water, climate change may be a significant factor influencing changes in the projected green water sustainability status, and the combination of socioeconomic drivers and climate change may significantly impact the projected blue water sustainability status across the basin. Projected changes in the green and blue water sustainability status have shown that more than 50% of the watershed will become ecologically fragile. In addition, the identified freshwater geographic sustainability hotspots may be beyond restoration without adequate long-term river basin water resource plans.

Global, regional and national burdens of non-melanoma skin cancer attributable to occupational exposure to solar ultraviolet radiation for 183 countries, 2000–2019: A systematic analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury

BackgroundA World Health Organization (WHO) and International Labour Organization (ILO) systematic review reported sufficient evidence for higher risk of non-melanoma skin cancer (NMSC) amongst people occupationally exposed to solar ultraviolet radiation (UVR). This article presents WHO/ILO Joint Estimates of global, regional, national and subnational occupational exposures to UVR for 195 countries/areas and the global, regional and national attributable burdens of NMSC for 183 countries, by sex and age group, for the years 2000, 2010 and 2019. MethodsWe calculated population-attributable fractions (PAFs) from estimates of the population occupationally exposed to UVR and the risk ratio for NMSC from the WHO/ILO systematic review. Occupational exposure to UVR was modelled via proxy of occupation with outdoor work, using 166 million observations from 763 cross-sectional surveys for 96 countries/areas. Attributable NMSC burden was estimated by applying the PAFs to WHO’s estimates of the total NMSC burden. Measures of inequality were calculated. ResultsGlobally in 2019, 1.6 billion workers (95 % uncertainty range [UR] 1.6–1.6) were occupationally exposed to UVR, or 28.4 % (UR 27.9–28.8) of the working-age population. The PAFs were 29.0 % (UR 24.7–35.0) for NMSC deaths and 30.4 % (UR 29.0–31.7) for disability-adjusted life years (DALYs). Attributable NMSC burdens were 18,960 deaths (UR 18,180–19,740) and 0.5 million DALYs (UR 0.4–0.5). Men and older age groups carried larger burden. Over 2000–2019, attributable deaths and DALYs almost doubled. ConclusionsWHO and the ILO estimate that occupational exposure to UVR is common and causes substantial, inequitable and growing attributable burden of NMSC. Governments must protect outdoor workers from hazardous exposure to UVR and attributable NMSC burden and inequalities.

A model for the infrared-radio correlation of main sequence galaxies at gigahertz frequencies and its variation with redshift and stellar mass

Context. The infrared-radio correlation (IRRC) of star-forming galaxies can be used to estimate their star formation rate (SFR) based on the radio continuum luminosity at MHz–GHz frequencies. For its practical application in future deep radio surveys, it is crucial to know whether the IRRC persists at high redshift z. Aims. Previous works have reported that the 1.4 GHz IRRC correlation of star-forming galaxies is nearly z-invariant up to z ≈ 4, but depends strongly on the stellar mass M⋆. This should be taken into account for SFR calibrations based on radio luminosity. Methods. To understand the physical cause behind the M⋆ dependence of the IRRC and its properties at higher z, we constructed a phenomenological model for galactic radio emission. Our model is based on a dynamo-generated magnetic field and a steady-state cosmic ray population. It includes a number of free parameters that determine the galaxy properties. To reduce the overall number of model parameters, we also employed observed scaling relations. Results. We find that the resulting spread of the infrared-to-radio luminosity ratio, q(z, M⋆), with respect to M⋆ is mostly determined by the scaling of the galactic radius with M⋆, while the absolute value of the q(z, M⋆) curves decreases with more efficient conversion of supernova energy to magnetic fields and cosmic rays. Additionally, decreasing the slope of the cosmic ray injection spectrum, αCR, results in higher radio luminosity, decreasing the absolute values of the q(z, M⋆) curves. Within the uncertainty range of our model, the observed dependence of the IRRC on M⋆ and z can be reproduced when the efficiency of supernova-driven turbulence is 5%, 10% of the kinetic energy is converted into magnetic energy, and αCR ≈ 3.0. Conclusions. For galaxies with intermediate to high (M⋆ ≈ 109.5 − 1011 M⊙) stellar masses, our model results in an IRRC that is nearly independent of z. For galaxies with lower masses (M⋆ ≈ 108.5 M⊙), we find that the IR-to-radio flux ratio increases with increasing redshift. This matches the observational data in that mass bin which, however, only extends to z ≈ 1.5. The increase in the IR-to-radio flux ratio for low-mass galaxies at z ≳ 1.5 that is predicted by our model could be tested with future deep radio observations.

Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for Sensor Data Analysis.

The paper explores the application of Steiner's most-frequent-value (MFV) statistical method in sensor data analysis. The MFV is introduced as a powerful tool to identify the most-common value in a dataset, even when data points are scattered, unlike traditional mode calculations. Furthermore, the paper underscores the MFV method's versatility in estimating environmental gamma background blue (the natural level of gamma radiation present in the environment, typically originating from natural sources such as rocks, soil, and cosmic rays), making it useful in scenarios where traditional statistical methods are challenging. It presents the MFV approach as a reliable technique for characterizing ambient radiation levels around large-scale experiments, such as the DEAP-3600 dark matter detector. Using the MFV alongside passive sensors such as thermoluminescent detectors and employing a bootstrapping approach, this study showcases its effectiveness in evaluating background radiation and its aptness for estimating confidence intervals. In summary, this paper underscores the importance of the MFV and bootstrapping as valuable statistical tools in various scientific fields that involve the analysis of sensor data. These tools help in estimating the most-common values and make data analysis easier, especially in complex situations, where we need to be reasonably confident about our estimated ranges. Our calculations based on MFV statistics and bootstrapping indicate that the ambient radiation level in Cube Hall at SNOLAB is 35.19 μGy for 1342 h of exposure, with an uncertainty range of +3.41 to -3.59μGy, corresponding to a 68.27% confidence level. In the vicinity of the DEAP-3600 water shielding, the ambient radiation level is approximately 34.80 μGy, with an uncertainty range of +3.58 to -3.48μGy, also at a 68.27% confidence level. These findings offer crucial guidance for experimental design at SNOLAB, especially in the context of dark matter research.

What constraints can one pose on the maximum mass of neutron stars from multimessenger observations?

ABSTRACT The maximum mass of neutron stars (MTOV) plays a crucial role in understanding their equation of state (EoS). Previous studies have used the measurements for the compactness of massive pulsars and the tidal deformability of neutron stars in binary neutron star (BNS) mergers to constrain the EoS and thus the MTOV. The discovery of the most massive pulsar, PSR J0952−0607, with a mass $\sim 2.35\, {\rm M}_{\odot }$, has provided a valuable lower limit for MTOV. Another efficient method to constrain MTOV is by examining the type of central remnant formed after a BNS merger. Gravitational wave (GW) data can provide the total mass of the system, while accompanying electromagnetic signals can help infer the remnant type. In this study, we combine all the previous constraints and utilize the observational facts that about 24 per cent of the short gamma-ray bursts are followed by an X-ray internal plateau, which indicate that roughly this fraction of BNS mergers yield supermassive neutron stars, to perform (Markov Chain) Monte Carlo simulations. These simulations allow us to explore the probability density distribution of MTOV and other parameters related to BNS mergers. Our findings suggest that MTOV is likely around $2.49\!-\!2.52\, {\rm M}_{\odot }$, with an uncertainty range of approximately [$-0.16$, $0.15\, {\rm M}_{\odot }$] ([$-0.28$, $0.26\, {\rm M}_{\odot }$]) at 1σ (2σ) confidence level. Furthermore, we examine the type of merger remnants in specific events like GW170817 and GW190425 to further constrain MTOV and other relevant parameters, which can help to understand the physical processes involved in BNS mergers.

Coverage uncertainty range: A new method for calculating uncertainty around summary statistics in healthcare quality indicators

Data from clinical health registries, such as medical quality registries, are often used as basis for healthcarequality indicators (QI). To aid the interpretation of quality indicators and support decisions, it is importantto quantify the uncertainty around the QI summary statistics. In this paper we suggest a novel method forquantifying such uncertainty: the Coverage uncertainty range. The method is based on the size of thepopulation present in the register relative to the total relevant population and does not make any assumptionsabout the sampling strategy or the value of the summary statistic. Furthermore, using both simulated dataand real-life data from a Norwegian medical quality registry, we illustrate why using confidence intervalswhen presenting healthcare quality indicators may lead to erroneous conclusions.

A strong secure path planning/following system based on type-3 fuzzy control, multi-switching chaotic systems, and random switching topology

This paper studies the synchronization and control of chaotic systems while proposing a novel chaotic-based path-tracking application for mobile robots (MRs) to ensure their safety and security. In security-based applications that use MRs, such as patrol MRs, the path of the MRs must be complex enough to prevent easy prediction. Multiple chaotic systems with a chaotic switching mechanism are introduced for secure path planning. The main challenges are that the dynamics of MRs are entirely unknown. The modeled dynamics of the MRs are unreliable in practice due to a broad range of uncertainties related to the parameters, operating conditions, environmental impacts, time delays, unmodeled frictions, noisy sensors, and faulty actuators. Also, the chaotic switching of reference signals between chaotic signals imposes a high dynamic perturbation. The main novelties are as follows: (1) a strong secure path is introduced for MRs. (2) A powerful fractional-order predictive controller using type-3 (T3) fuzzy-logic systems (FLSs) is developed. (3) The estimation and prediction errors of T3-FLSs are compensated by a designed parallel compensator. (4) T3-FLSs are tuned online, such that stability is ensured, and prediction accuracy is guaranteed. (5) The suggested scheme is implemented on a real-world MR, and the results demonstrate the feasibility and accuracy of the proposed method. Also, in several simulations, the efficacy of the introduced controller is examined.

The impact of informality and institutional quality on environmental footprint: The case of emerging economies in a comparative approach

The endeavor to implement the 2030 Agenda of national and international stakeholders became increasingly impetuous, considering the wide range of uncertainties and risks. The new humans-centered development model built on the prominence of environmental and social values seeks to reinforce communities’ resilience and mitigate environmental risks, leaving no one behind. For this to happen, solid and effective institutions, the right environmental policies, and a safe statutory labor framework are the sine qua non. In this study, we evaluated the effects of informality, institutional quality, and renewable energy consumption on ecological footprint of two groups of emerging countries from Europe and Asia from 2002 to 2018. Our results by PMG-ARDL approach highlight dissimilarities between the two groups, showing greater heterogeneity. In this sense, informality is found to have positive and significant effects on ecological footprint in the long run in emerging European countries. In contrast, the effect is negative for emerging Asian countries. In the short run, the effects are less critical in the studied countries between the two groups. Institutional quality variables impacted environmental quality differently. In this sense, it is detrimental for policymakers to consider concerted measures to decrease institutional vulnerabilities and reduce the level of the informal economy. The outcome of this study concurs with a complete awareness of the importance of informality and institutional quality to mitigate social and environmental risks conjunctively.

Economic Burden Associated With Untreated Mental Illness in Indiana

There is a paucity of systematically captured data on the costs incurred by society-individuals, families, and communities-from untreated mental illnesses in the US. However, these data are necessary for decision-making on actions and allocation of societal resources and should be considered by policymakers, clinicians, and employers. To estimate the economic burden associated with untreated mental illness at the societal level. This cross-sectional study used multiple data sources to tabulate the annual cost of untreated mental illness among residents (≥5 years old) in Indiana in 2019: the US National Survey on Drug Use and Health, the National Survey of Children's Health, Indiana government sources, and Indiana Medicaid enrollment and claims data. Data analyses were conducted from January to May 2022. Direct nonhealth care costs (eg, criminal justice system, homeless shelters), indirect costs (unemployment, workplace productivity losses due to absenteeism and presenteeism, all-cause mortality, suicide, caregiver direct health care, caregiver productivity losses, and missed primary education), and direct health care costs (disease-related health care expenditures). The study population consisted of 6 179 105 individuals (median [SD] age, 38.0 [0.2] years; 3 132 806 [50.7%] were women) of whom an estimated 429 407 (95% CI, 349 526-528 171) had untreated mental illness in 2019. The economic burden of untreated mental illness in Indiana was estimated to be $4.2 billion annually (range of uncertainty [RoU], $2.1 billion-$7.0 billion). The cost of untreated mental illness included $3.3 billion (RoU, $1.7 billion-$5.4 billion) in indirect costs, $708.5 million (RoU, $335 million-$1.2 billion) in direct health care costs, and $185.4 million (RoU, $29.9 million-$471.5 million) in nonhealth care costs. This cross-sectional study found that untreated mental illness may have significant financial consequences for society. These findings put into perspective the case for action and should be considered by policymakers, clinicians, and employers when allocating societal resources and funding. States can replicate this comprehensive framework as they prioritize key areas for action regarding mental health services and treatments.

Atmospheric Hydroxyl Radical Reaction Rate Coefficient and Total Environmental Lifetime of α-Endosulfan.

Endosulfan is a persistent organochlorine pesticide that was globally distributed before it was banned and continues to cycle in the Earth system. The chemical kinetics of the gas-phase reaction of α-endosulfan with the hydroxyl radical (OH) was studied by means of pulsed vacuum UV flash photolysis and time-resolved resonance fluorescence (FP-RF) as a function of temperature in the range of 348-395 K and led to a second-order rate coefficient kOH = 5.8 × 10-11 exp(-1960K/T) cm3 s-1 with an uncertainty range of 7 × 10-12 exp(-1210K/T) to 4 × 10-10 exp(-2710K/T) cm3 s-1. This corresponds to an estimated photochemical atmospheric half-life in the range of 3-12 months, which is much longer than previously assumed (days to weeks). Comparing the atmospheric concentrations observed after the global ban of endosulfan with environmental multimedia model predictions, we find that photochemical degradation in the atmosphere is slower than the model-estimated biodegradation in soil or water and that the latter limits the total environmental lifetime of endosulfan. We conclude that the lifetimes typically assumed for soil and aquatic systems are likely underestimated and should be revisited, in particular, for temperate and warm climates.

Non-parametric projections of the net-income distribution for all U.S. states for the Shared Socioeconomic Pathways

Income distributions are a growing area of interest in the examination of equity impacts brought on by climate change and its responses. Such impacts are especially important at subnational levels, but projections of income distributions at these levels are scarce. Here, we project U.S. state-level income distributions for the Shared Socioeconomic Pathways (SSPs). We apply a non-parametric approach, specifically a recently developed principal components algorithm to generate net income distributions for deciles across 50 U.S. states and the District of Columbia. We produce these projections to 2100 for three SSP scenarios in combination with varying projections of GDP per capita to represent a wide range of possible futures and uncertainties. In the generation of these scenarios, we also generated tax adjusted historical deciles by U.S. states, which we used for validating model performance. Our method thus produces income distributions by decile for each state, reflecting the variability in state income, population, and tax regimes. Our net income projections by decile can be used in both emissions- and impact-related research to understand distributional effects at various income levels and identify economically vulnerable populations.

Cost-effectiveness of seasonal influenza vaccination of children in China: a modeling analysis

BackgroundChina has a high burden of influenza-associated illness among children. We aimed to evaluate the cost-effectiveness of introducing government-funded influenza vaccination to children in China (fully-funded policy) compared with the status quo (self-paid policy).MethodsA decision tree model was developed to calculate the economic and health outcomes, from a societal perspective, using national- and provincial-level data. The incremental cost-effectiveness ratio (ICER) [incremental costs per quality-adjusted life year (QALY) gained] was used to compare the fully-funded policy with the self-paid policy under the willingness-to-pay threshold equivalent to national and provincial GDP per capita. Sensitivity analyses were performed and various scenarios were explored based on real-world conditions, including incorporating indirect effect into the analysis.ResultsCompared to the self-paid policy, implementation of a fully-funded policy could prevent 1,444,768 [95% uncertainty range (UR): 1,203,446–1,719,761] symptomatic cases, 92,110 (95% UR: 66,953–122,226) influenza-related hospitalizations, and 6494 (95% UR: 4590–8962) influenza-related death per season. The fully-funded policy was cost-effective nationally (7964 USD per QALY gained) and provincially for 13 of 31 provincial-level administrative divisions (PLADs). The probability of a funded vaccination policy being cost-effective was 56.5% nationally, and the probability in 9 of 31 PLADs was above 75%. The result was most sensitive to the symptomatic influenza rate among children under 5 years [ICER ranging from − 25,612 (cost-saving) to 14,532 USD per QALY gained]. The ICER of the fully-funded policy was substantially lower (becoming cost-saving) if the indirect effects of vaccination were considered.ConclusionsIntroducing a government-funded influenza policy for children is cost-effective in China nationally and in many PLADs. PLADs with high symptomatic influenza rate and influenza-associated mortality would benefit the most from a government-funded influenza vaccination program.

A three-layer planning framework for regional integrated energy systems based on the quasi-quantum theory

With the rapid development in social informatization, more and more factors, such as regional economy, technological development, and people’s living needs, will affect the supply–demand relationship of regional integrated energy systems (RIES), which involve multiple energy forms. These factors turn the supply–demand relationship of an energy system into a nonlinear and time-varying chaotic system. This makes it difficult to predict and balance these relationships, which poses a huge challenge to the prediction, planning, operation, etc. Existing conventional methods attempt to quantify the prediction bias caused by various external environmental factors of energy systems and to weaken the uncertainty caused by multiple energy loads through random programming. However, uncertainty factors increase with the development of an energy system, thereby inreasing the requirements of such uncertainty quantification methods. Furthermore, in conventional methods, the prediction or planning decisions are often made by an observer while neglecting the impact of the observer’s decision-making behavior on prediction and planning. Therefore, this paper proposes a three-layer planning framework for to solve the above problems. This architecture includes quasi-quantum uncertainty periodic evaluation, stochastic planning based on information gap decision theory, and rolling planning based on model predictive control. First, we establish a quasi-quantum model of multi-energy system prediction error based on the quasi-quantum wave function model to qualitatively analyze prediction errors affected by uncertainty before and after planning. Simultaneously, combined with the evaluation model of the quasi-quantum potential energy function, a quasi-quantum uncertainty period evaluation model is proposed. Based on the minimum equivalent planning entropy, the planning cycle of multistage rolling planning is divided to minimize uncertainty. Second, the information gap decision theory is used to quantify the influence range of source and load uncertainty in the planning process and the multistage planning cycle is randomly planned. Then, the model predictive control method is used to control the actual error, and the planning strategy is changed in time to reduce the planning deviation caused by the prediction error. Finally, adopting a Beichen demonstration area in Tianjin, China, as a case study, the effectiveness of the proposed method in uncertainty analysis and long-term planning improvement is verified. The three-layer planning framework can improve the adaptability of the regional integrated energy system in the long-term planning process, and can timely adjust the planning scheme to cope with the impact of unpredictable uncertainties in the planning process.© 2017 Elsevier Inc. All rights reserved.

Measurement of Magnesium, Zinc, and Copper in Human Serum by Using Isotope Dilution Inductively Coupled Plasma Mass Spectrometry (ID ICP-MS).

In order to evaluate the reliability of the ID ICP-MS method for the measurement of magnesium, zinc, and copper in human serum, we investigated the traceability, precision, trueness, and uncertainty of the method. This method traces the contents of magnesium, zinc, and copper in human serum to the standard materials NIST SRM3131a, SRM3168a, and SRM3114 respectively, thus completing the traceability to SI unit. The repeatability of this method for measuring magnesium, zinc, and copper in the human serum reference material GBW09152 was found to be 0.2%, 0.7%, and 0.6% (n = 9), respectively. The measurement, when employed to measure the magnesium, zinc, and copper in standard materials, had caused a maximum deviation of less than 0.88%, 1.35%, and 1.15%, respectively. The measurement results are within the stated uncertainty range of standard materials. The expanded uncertainties were 0.2 mg·kg-1, 0.04 mg·kg-1, and 0.08 mg·kg-1 (K = 2) for magnesium, zinc, and copper, respectively. Therefore, this method has high trueness, good reproducibility, and simple operation and is suitable for tracing the values of magnesium, zinc, and copper in human serum.

T-shaped partial least squares for high-dosed new active pharmaceutical ingredients in continuous twin-screw wet granulation: Granule size prediction with limited material information

This work presents a granule size prediction approach applicable to diverse formulations containing new active pharmaceutical ingredients (APIs) in continuous twin-screw wet granulation. The approach consists of a surrogate selection method to identify similar materials with new APIs and a T-shaped partial least squares (T-PLS) model for granule size prediction across varying formulations and process conditions. We devised a surrogate material selection method, employing a combination of linear pre-processing and nonlinear classification algorithms, which effectively identified suitable surrogates for new materials. Using only material properties obtained through four characterization methods, our approach demonstrated its predictive prowess. The selected surrogate methods were seamlessly integrated with our developed T-PLS model, which was meticulously validated for high-dose formulations involving three new APIs. When surrogating new APIs based on Gaussian process classification, we achieved the lowest prediction errors, signifying the method’s robustness. The predicted d-values were within the range of uncertainty bounds for all cases, except for d90 of API C. Notably, the approach offers a direct and efficient solution for early-phase formulation and process development, considerably reducing the need for extensive experimental work. By relying on just four material characterization methods, it streamlines the research process while maintaining a high degree of accuracy.

Dwarf galaxy archaeology from chemical abundances and star-formation histories

ABSTRACT We model the stellar abundances and ages of two disrupted dwarf galaxies in the Milky Way stellar halo: Gaia-Sausage Enceladus (GSE) and Wukong/LMS-1. Using a statistically robust likelihood function, we fit one-zone models of galactic chemical evolution with exponential infall histories to both systems, deriving e-folding time-scales of τin = 1.01 ± 0.13 Gyr for GSE and $\tau _\text{in} = 3.08^{+3.19}_{-1.16}$ Gyr for Wukong/LMS-1. GSE formed stars for $\tau _\text{tot} = 5.40^{+0.32}_{-0.31}$ Gyr, sustaining star formation for ∼1.5–2 Gyr after its first infall into the Milky Way ∼10 Gyr ago. Our fit suggests that star formation lasted for $\tau _\text{tot} = 3.36^{+0.55}_{-0.47}$ Gyr in Wukong/LMS-1, though our sample does not contain any age measurements. The differences in evolutionary parameters between the two are qualitatively consistent with trends with stellar mass M⋆ predicted by simulations and semi-analytic models of galaxy formation. Our inferred values of the outflow mass-loading factor reasonably match $\eta \propto M_\star ^{-1/3}$ as predicted by galactic wind models. Our fitting method is based only on Poisson sampling from an evolutionary track and requires no binning of the data. We demonstrate its accuracy by testing against mock data, showing that it accurately recovers the input model across a broad range of sample sizes (20 ≤ N ≤ 2000) and measurement uncertainties (0.01 ≤ σ[α/Fe], σ[Fe/H] ≤ 0.5; $0.02 \le \sigma _{\log _{10}(\text{age})} \le 1$). Due to the generic nature of our derivation, this likelihood function should be applicable to one-zone models of any parametrization and easily extensible to other astrophysical models which predict tracks in some observed space.

Thermo-hydro-mechanical modelling of geothermal energy extraction in deep mines in spatially heterogeneous settings

With the increasing demand for mineral and alternative energy resources, as well as the gradual depletion of shallow resources, the exploitation and utilization of mineral resources and geothermal energy in deep strata is an effective way to solve the problem of resource shortage [1]. In recent years, as a new type of resource mining mode, the co-mining of deep mineral and geothermal energy has developed rapidly [2, 3]. This method can make use of the original equipment of the mine for geothermal exploitation. However, the deep co-mining system faces two significant challenges: the first is the significant uncertainty inherent in subsurface properties, while the second is the high levels of geostress and temperature associated with deep mining. These challenges are adding some constraints on the practicality of exploiting such systems and limit the feasibility of deep resource co-mining, so that modelling efforts are needed for actual risk assessment.
 Consequently, we developed a Thermo-Hydro-Mechanical (THM) coupling framework for geothermal energy exploitation in deep mines using COMSOL to quantitatively characterize the temperature field of the geothermal system and predict the stress field of the mining system, considering the joint effects of large uncertainties and THM coupling. Through SGeMS, the uncertainty and spatial heterogeneity distribution of porosity are first generated. Then, the uncertainty of the hydraulic parameter [4] (permeability), mechanical parameter [5] (elastic modulus), and thermal parameter [6] (heat capacity and heat conductivity) was derived from the porosity. 500 samples were generated within a given uncertainty range, by means of Monte Carlo simulations. The spatial and temporal distributions of the temperature field of the geothermal system, and the stress field of the mining system were simulated, for each sample with COMSOL. Using the distance-based global sensitivity analysis [6], the most sensitive parameters for deep mining are identified, the heat storage capacity of the system and evolution of the maximum stress ratio are evaluated, including uncertainty.

Important drivers of East African monsoon variability and improving rainy season onset prediction

Monsoon rain and its year-to-year variability have a profound influence on Africa’s socio-economic structure by heavily impacting sectors such as agricultural and energy. This study focuses on major drivers of the east African monsoon during October-November-December (OND) which is the standard time window for the onset of the rainy season, be it unimodal or bimodal. Two drivers viz. Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO) both separately indicate very strong positive connections with monsoon (OND) rain not only in the OND season with zero seasonal lag, but the signal is also present even taking IOD and ENSO a season ahead. A compositing approach is applied that can additionally identify strong signals when different combinations of ENSO and IOD phases act as confounding factors. Results of precipitation anomaly suggest that when IOD and ENSO are both on the same phase in July-August-September (JAS), a significant OND rainfall anomaly occurs around the east African sector: A deficit (excess) of OND monsoon rain occurs when both drivers are in a negative (positive) phase during JAS. A location Kibaha in Tanzania, for which station data are available, is considered for a more in-depth analysis. The uncertainty range in cumulative OND rainfall is also reduced to a large degree when IOD and ENSO phases are both negative in JAS. These results can be used for prediction purposes and interestingly, that criterion of IOD and ENSO being of same phase in JAS was again matched in 2022 (both negative) and hence it was possible to deliver early warnings for a deficit in rainfall a season ahead. Techniques to compute the monsoon onset as determined by meteorological services such as the Tanzania Meteorological Authority rely on various thresholds, which may also vary by country. To overcome some of the issues with thresholds-based techniques, other definitions of ‘onset’ take into account cumulative rainfall amount and such technique has also been tested and compared. In both approaches, late (early) onsets dominate in years when ENSO and IOD are both negative (positive) during JAS. In these cases, it is therefore possible to provide an estimation of cumulative rainfall and onset for OND in terms of average, median value, range and distribution of rainfall one season in advance. Such results have implications for optimizing agricultural, water and energy management, also mitigating possible severe production losses, which would impact the livelihoods of millions of Africans.