Profile Estimation Research Articles

Probabilistic machine learning aided transformer lifetime prediction framework for wind energy systems

Accurate lifetime prediction of transformers operated in power grids with renewable energy systems is a challenging task because it requires a large amount of data that is not usually available. In the case of wind energy, this complexity is intensified with the stochastic ageing process influenced by the intermittency of the wind and weather conditions. Existing models make use of detailed power topologies to evaluate transformer stress profiles and associated degradation. However, this modelling approach requires high computational resources and long simulation times. In this context, this paper presents a lifetime prediction model for transformers designed through probabilistic machine learning, thermal modelling and ageing analysis. The proposed model is compared with synthetic wind-to-power detailed simulations of a wind farm and validated with real data. The lifetime prediction is evaluated with different mission profile estimates and results show that the accuracy of the probabilistic machine learning model is very high, with an error of 0.47% for the median value and 80% prediction interval errors within 6%–7% with respect to observations. Moreover, there is a substantial reduction in the simulation time and memory requirements when compared to the synthetic model. A detailed sensitivity analysis demonstrates the influence on transformer ageing of different overloading strategies, thermal constants and the geographic location of the wind farm.

A GIS Open-Data Co-Simulation Platform for Photovoltaic Integration in Residential Urban Areas

• Advanced co-simulation of rooftop PV production and households electricity demand. • Urban Energy Systems design and RES integration in the context of future smart cities. • Enabling considerations on Demand Side Management and Demand Response strategies. • High spatio-temporal resolution (25 cm and 10 minutes respectively) • Integration in a GIS environment of open-data and models. The rising awareness of environmental issues and the increase of renewable energy sources (RES) has led to a shift in energy production toward RES, such as photovoltaic (PV) systems, and toward a distributed generation (DG) model of energy production that requires systems in which energy is generated, stored, and consumed locally. In this work, we present a methodology that integrates geographic information system (GIS)-based PV potential assessment procedures with models for the estimation of both energy generation and consumption profiles. In particular, we have created an innovative infrastructure that co-simulates PV integration on building rooftops together with an analysis of households’ electricity demand. Our model relies on high spatiotemporal resolution and considers both shadowing effects and real-sky conditions for solar radiation estimation. It integrates methodologies to estimate energy demand with a high temporal resolution, accounting for realistic populations with realistic consumption profiles. Such a solution enables concrete recommendations to be drawn in order to promote an understanding of urban energy systems and the integration of RES in the context of future smart cities. The proposed methodology is tested and validated within the municipality of Turin, Italy. For the whole municipality, we estimate both the electricity absorbed from the residential sector (simulating a realistic population) and the electrical energy that could be produced by installing PV systems on buildings’ rooftops (considering two different scenarios, with the former using only the rooftops of residential buildings and the latter using all available rooftops). The capabilities of the platform are explored through an in-depth analysis of the obtained results. Generated power and energy profiles are presented, emphasizing the flexibility of the resolution of the spatial and temporal results. Additional energy indicators are presented for the self-consumption of produced energy and the avoidance of CO 2 emissions.

A Linear Variable Parameter Observer-Based Road Profile Height Estimation for Suspension Nonlinear Dynamics Improvements

The accuracy of road profile height estimation has a significant impact on suspension control quality, which in turn influences handling, stability of vehicle motion and occupants' comfort. Based on our previous published work, this article designs and proposes a novel method for road profile estimation that is based on suspension nonlinear dynamics. The proposed approach first establishes the road profile height estimation model considering a real suspension design, including nonlinear characteristics of the shock absorber, bushing stiffness and damping, actual geometry of the control arms, and positioning of absorber. Unlike the conventional sprung-unsprung mass model, the real nonlinear suspension model provides much higher output prediction accuracy as computational and experimental results demonstrated. Moreover, the established estimation method and model require only two easily measurable parameters, which are the acceleration of the sprung mass and the travel of the shock absorber. While considering nonlinearity and hysteretic characteristics of the damping force, the proposed approach is based on the estimation framework of linear parameter-varying systems and establishes a sliding mode observer for estimating the road profile height. The stability of estimation error dynamics of the observer is guaranteed by the Lyapunov stability analysis. As bench-test experiments of a hardware implementation of the suspension-wheel unit validated the computational results. The tests demonstrated that the proposed method allows for real-time estimating of the road profile height in different road conditions and electric currents of the suspension damper and is robust to uncertainty of the payload.

Evaluation of the performance of F-layer peak height models used in IRI-2016 over the Indian equatorial and low latitudes

The performance of different height models (BSE-1979, AMTB-2013, and SHU-2015) in the International Reference Ionosphere (IRI-2016) is evaluated over the Indian equatorial and low latitude sectors. The impact of deviations in the modelled hmF2 on the estimation of electron density profiles and the Total Electron Content (TEC) is examined using the three different hmF2 methods in IRI-2016. For this study digisonde observations over Trivandrum (8.5 °N, 76.9 °E) and Ahmedabad (23.0 °N, 72.5 °E) along with GPS observations over Colombo (6.8 °N, 79.8 °E) and Ahmedabad during a high solar activity year 2014 are considered. A comparison of electron density profiles between digisonde and IRI-2016 at both the equatorial station, Trivandrum, and the low latitude station, Ahmedabad, revealed significant deviations with higher deviations in the post sunset hours at the equator. The three height methods have shown significant differences in their predictions of hmF2 at both locations. At the equator, the SHU-2015 method shows better estimates closer to the measurement values, than the other two methods except at Pre-Reversal Enhancement (PRE) time, during which, the AMTB-2013 method shows better performance. At the low latitude station, the newly added methods (SHU-2015 and AMTB-2013) do not indicate any improvements in estimating the hmF2. The availability of three different height methods in IRI-2016 is exploited to understand the modifications if any, in the ionospheric height structure and TEC for different hmF2 methods and the corresponding results are presented. As the variations in hmF2 are used to derive vertical drift on many occasions in the absence of incoherent scatter radar, this study is significant in assessing the relative efficacy of the three height models and hence useful to understand various ionospheric processes over the low/equatorial latitudes.

Physics-informed neural networks for integrated traffic state and queue profile estimation: A differentiable programming approach on layered computational graphs

This paper presents an integrated framework for physics-informed joint traffic state and queue profile estimation (JSQE) on freeway corridors, utilizing heterogeneous data sources. The integrated modeling framework aims to maximize the benefits of information from physics-informed analytical traffic flow models and field observations while leveraging joint estimation spaces. Potential inconsistencies between different modeling components must be acknowledged and carefully managed to ensure model feasibility. To minimize such inconsistencies, a nonlinear programming model is formulated for the JSQE problem, taking into account traffic flow models and observations from both corridor and local segment levels. At the corridor level, a fluid queue approximation is employed to model queuing dynamics. Assuming polynomial arrival and departure rates, critical system variables such as time-dependent delay, travel time, and queue length are analytically derived. To preserve the differentiability of traffic state variables, continuous space–time distribution functions are introduced to model traffic flow variables and partial differential equations. A computational graph is constructed to represent the nonlinear programming model in a layered structure, which is then solved using a forward–backward method. Comprehensive numerical experiments based on real-world and hypothetical datasets are designed to demonstrate the effectiveness of the proposed framework.

Constrained profile estimation for distributed parameter system in one dimension using orthogonal collocation

Several industrial processes fall under the category of Distributed parameter systems (DPS) which are represented as coupled partial differential equations (PDEs). Obtaining accurate and physically meaningful profile estimates of states despite only a limited number of spatially distributed measurements, is a pre-requisite for adequately controlling such systems. In this work, we propose an online constrained state profile estimation approach for DPS. The approach is based on reduced-dimension model represented by differential and algebraic equations (DAE) which in turn is developed by discretization of the PDEs along the spatial coordinate using the orthogonal collocation (OC) method. A profile prediction error based approach is proposed for systematically fixing the dimension of the OC model to be used. Further, to compensate for the inevitable model plant mismatch (MPM) arising in the process of the dimensionality reduction, additive zero mean Gaussian white noise processes are introduced in both state dynamics as well as measurement model. Statistics of these noises are obtained based on simulation based realizations of approximation errors at collocation points and measurement locations. To incorporate the identified correlation between the resulting state and measurement noises in the estimation method, we propose a constrained DAE-EKF (extended Kalman Filter) extension to deal with such correlations. The estimation approach also involves an appropriately formulated optimization based update step to ensure constraint satisfaction over the entire spatial profile leading to physically meaningful state profile estimates. The constrained profile estimator is also integrated with an online moving window based parameter estimation, which enables tracking of both time varying parameters and state profiles. The efficacy of the proposed approach is demonstrated by conducting stochastic simulation studies using two tubular reactor systems. The results show that the proposed constrained online state estimation approach is able to obtain accurate profile estimates at low computational cost, while ensuring that the profile estimates satisfy the given constraints.

Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product

Abstract. The Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission is a joint endeavour developed by the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA) and features a 94 GHz Doppler Cloud Profiling Radar. This paper presents the theoretical basis of the cloud and precipitation microphysics (C-CLD) EarthCARE Level 2 (L2) algorithm. The C-CLD algorithm provides the best estimates of the vertical profiles of water mass content and hydrometeor characteristic size, obtained from radar reflectivity, path-integrated signal attenuation and hydrometeor sedimentation Doppler velocity estimates using optimal estimation (OE) theory. To obtain the forward model relations and the associated uncertainty, an ensemble-based method is used. This ensemble consists of a collection of in situ measured drop size distributions that cover natural microphysical variability. The ensemble mean and standard deviation represent the forward model relations and their microphysics-based uncertainty. The output variables are provided on the joint standard grid horizontal and EarthCARE Level 1b (L1b) vertical grid (1 km along track and 100 m vertically). The OE framework is not applied to liquid-only clouds in drizzle-free and lightly drizzling conditions, where a more statistical approach is preferred.

A web application for sex and stature estimation from radiographic proximal femur for a Thai population

In both forensic and archaeological domains, the discovery of incomplete human remains is a frequent occurrence. Nevertheless, the estimation of biological profiles from such remains presents a challenge due to the absence of crucial skeletal elements, such as the skull and pelvis. This study aimed to assess the utility of the proximal femur in the forensic identification process by creating a web application for osteometric analysis of the proximal femur. The aim was to determine the sex and stature of an individual from radiographs of the left anteroposterior femur. To accomplish this, an automated method was developed for acquiring linear measurements from radiographic images of the proximal femur using Python tools. The application of Hough techniques and Canny edge detection was utilized to generate linear femoral dimensions from radiographs. A total of 354 left femora were radiographed and measured by the algorithm. The sex classification model employed in this study was the Naïve Bayes algorithm (accuracy = 91.2 %). Results indicated that Gaussian process regression (GPR) was the most effective method for estimating stature (mean error = 4.68 cm, SD = 3.93 cm). The proposed web application holds the potential to serve as a valuable asset in the realm of forensic investigations in Thailand, particularly in the estimation of biological profiles from fragmentary skeletal remains.

Association between 10‐year cardiovascular mortality‐risk and neuroimaging biomarkers in the FINGER trial

AbstractBackgroundCardiovascular risk factors are associated with cognitive decline and dementia. Timely estimation and targeting of cardiovascular risk burden may contribute towards the prevention of cognitive decline. The cardiovascular risk scores of the European Society of Cardiology’s, SCORE (for adults up to 64 years) and SCORE‐OP (for older adults aged 65 years or more), are commonly used tool for estimation of 10‐year CVD mortality and may reflect the impact of cardiovascular risk burden on brain pathology if validated against neuroimaging biomarkers.MethodThis study investigated associations between (i) baseline SCORE/SCORE‐OP and baseline neuroimaging biomarkers (brain magnetic resonance imaging [MRI] and Pittsburgh Compound B‐positron emission tomography [PiB‐PET] measures) (ii) change in SCORE/SCORE‐OP and change in neuroimaging measures during the 2‐year Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER). FINGER targeted at‐risk older adults, aged 60‐77 years, from the general population. Participants were randomized to either multidomain intervention (diet, exercise, cognitive training, and vascular risk management) or control group (general health advice). Neuroimaging (MRI and PiB‐PET) data from baseline and 2‐year visits were used. A total of 112 participants had repeated brain MRI measures (hippocampus, total grey matter, and white matter lesion volumes, and Alzheimer’s disease signature cortical thickness). Repeated PiB‐PET scans were available for 39 participants.ResultPreliminary results suggest that at baseline higher SCORE/SCORE‐OP, indicating increased risk of CVD mortality was significantly associated with lower hippocampus volume (estimate = ‐0.010; p = 0.004), lower total grey matter volume (estimate = ‐0.016; p = 0.007), higher white matter lesion volume (estimate = 0.048; p = 0.015), and lower AD signature cortical thickness (‐0.004; p = 0.048) but not with PiB‐PET. No significant associations were found for change in neuroimaging measures and baseline SCORE/SCORE‐OP or change in SCORE/SCORE‐OP during the 2‐year follow‐up.ConclusionCardiovascular risk scores need to be validated against different brain pathology markers in diverse populations. This will enable more precise identification and estimation of dementia risk profiles, guiding at‐risk persons to precise intervention strategies, good for both, heart, and brain.

Performance Limit of Fiber-Longitudinal Power Profile Estimation Methods

This paper presents analytical results on longitudinal power profile estimation (PPE) methods, which visualize signal power evolution in optical fibers at a coherent receiver. The PPE can be formulated as an inverse problem of the nonlinear Schr\"odinger equation, where the nonlinear coefficient (and thus signal power) is reconstructed from boundary conditions, i.e., transmitted and received signals. Two types of PPE methods are reviewed and analyzed, including correlation-based methods (CMs) and minimum-mean-square-error-based methods (MMSEs). The analytical expressions for their output power profiles and spatial resolution are provided, and thus the theoretical performance limits of the two PPE methods and their differences are clarified. The derived equations indicate that the estimated power profiles of CMs can be understood as the convolution of a true power profile and a smoothing function. Consequently, the spatial resolution and measurement accuracy of CMs are limited, even under noiseless and distortionless conditions. Closed-form formulas for the spatial resolution of CMs are shown to be inversely proportional to the product of a chromatic dispersion coefficient and the square of signal bandwidth. With MMSEs, such a convolution effect is canceled out and the estimated power profiles approach a true power profile under a fine spatial step size.

In silico screening, ADMET analysis and MD simulations of phytochemicals of Onosma bracteata Wall. as SARS CoV-2 inhibitors.

Being attracted with their cardiotonic, antidiabetic, cough relieving activity, treatment of fever, absorbent, anti-asthmatic, etc. activities reported in ancient Ayurvedic literature, phytochemicals of Onosma bracteata wall should be evaluated for their activity against SARS-CoV-2 virus. The main objective of this study is to identify a hit molecule for the inhibition of entry, replication, and protein synthesis of SARS CoV-2 virus into the host. To achieve given objective, computational virtual screening of phytochemicals of Onosma bracteata wall has been performed against three main viral targets: spike, RdRp, and Mpro. Further, the analysis of Lipinski's Ro5 and their estimation of ADMET profiles were performed using computational tools. The MD simulations studies of top hits against each viral target have also been performed for 20ns to ensure their stability. The analysis of results revealed that Pulmonarioside C (9) and other plant compounds showed better binding affinity towards targets than existing antiviral compounds, making them probable lead compounds against SARS-CoV-2. Structural modifications and studies through in silico analysis provided the founding stone for the establishment of SARS CoV-2 inhibitory potential of phytoconstitutents of Onosma bracteata wall.

Retrieval and validation of vertical LAI profile derived from airborne and spaceborne LiDAR data at a deciduous needleleaf forest site

ABSTRACT Leaf area index (LAI) is defined as one half of the total green leaf area per unit ground surface area. Its vertical profile is critical for understanding the remote sensing radiative transfer processes. LAI profile has been derived from airborne and spaceborne LiDAR data, such as the Global Ecosystem Dynamics Investigation (GEDI) installed on the International Space Station. However, the capability of various algorithms for the LAI profile estimation with airborne LiDAR is not clearly evaluated, and the estimated LAI profiles, including the GEDI LAI products, are not been fully validated. This study conducted a quantitative retrieval and validation of the LAI profiles using terrestrial and airborne laser scanning (TLS and ALS) and spaceborne GEDI data over a deciduous needleleaf forest site in northern China. The vertical LAI profile was estimated in the field using an upward digital hemispherical photography (DHP) attached to a portable measurement system in 2020 and 2021. A suite of new LiDAR indices combining both LiDAR return number and return intensity was explored for the LAI profile estimation. All LAI profiles obtained from the DHP, TLS, ALS, and GEDI during the leaf-on season and leaf-off season were compared. The DHP shows a good agreement with the TLS LAI profiles (R2 = 0.97). The LAI profile derived from the ALS data using the combined light penetration index (LPIRI) agrees well (R2 ≥0.86) with the DHP, TLS, and GEDI estimates. In general, the LPIRI is advantageous for regional LAI profile mapping from ALS. The GEDI cumulative LAI corresponds well with the DHP during the leaf-on season (R2 = 0.90, RMSE = 0.23), but underestimates during the leaf-off season (R2 = 0.70, RMSE = 0.14, bias=−0.13). The underestimation is attributed to the higher canopy and ground reflectance ratio (ρv/ρg) assigned in the algorithm and the height discrepancy between the GEDI and field measurements. For the GEDI LAI profile product, further validation and improvement are necessary for other biome types and landscape conditions, especially during the leaf-off season.

Attenuative effect of astilbin on polystyrene microplastics induced testicular damage: Biochemical, spermatological and histopathological-based evidences

Polystyrene microplastics (PS-MPs) are the potential environmental pollutants that possess the ability to induce testicular damage. Astilbin (ASB) is a dihydroflavonol, abundantly reported in multiple plants that has various pharmacological properties. This research elucidated the mitigative potential of ASB against PS-MPs-instigated testicular toxicity. 48 adult male rats (200 ± 10 g) were distributed into 4 groups (n = 12): control, PS-MPs received (0.01 mg/kg), PS-MPs + ASB received (0.01 mg/kg + 20 mg/kg) and ASB supplemented group (20 mg/kg). After 56th day of the trial, animals were sacrificed and testes were harvested for the estimation of biochemical, hormonal, spermatogenic, steroidogenic, apoptotic and histological profiles. PS-MPs intoxication significantly (P < 0.05) lowered glutathione peroxidase (GPx), superoxide dismutase (SOD), glutathione reductase (GSR) as well as catalase (CAT) activities, whereas elevated MDA as well as ROS levels. Besides, the levels of interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), nuclear factor kappa-B (NF-κB) along with cyclooxygenase-2 (COX-2) activity were raised. PS-MPs treatment reduced luteinizing hormone (LH), plasma testosterone and follicle-stimulating hormone (FSH) level besides decreased epididymal sperm number, viability, motility as well as the count of HOS coil-tailed spermatozoa and increased sperm morphological irregularities. PS-MPs exposure lowered steroidogenic enzymes (17β-HSD, 3β-HSD and StAR protein along with Bcl-2 expression, besides increasing Caspase-3 and Bax expressions and histopathological alterations in testicular tissues. However, ASB treatment significantly reversed PS-MPs mediated damage. In conclusion, ASB administration is protective against PS-MPs-instigated testicular damage owing to its anti-inflammatory, anti-apoptotic, antioxidant and androgenic nature.

Estimation of heating system energy modeling profiles based on environmental monitoring records in Central-Southern Chile

Data simulated for occupancy profiles, usually based on standard occupancy schedules, must be validated against real measurements, and many studies have pointed out the gap between them. This problem is more pronounced in homes that are not heated by grid-connected energy sources, such as wood-burning stoves, because the meter cannot provide a real-time estimate. In Central-South Chile, 74% of homes are heated by wood stoves, causing an acute problem of air pollution in several urban areas. The government is trying to solve this problem, but at the moment there is no data on occupancy profiles, hence it is not possible to estimate the energy intensity for heating. This exploratory study aims to clarify to what extent occupancy profiles can be estimated from PM 2.5 pollution levels. To this end, publicly available data on PM 2.5 concentrations in 17 cities in central-south Chile was used as a proxy to build occupancy schedules for homes heated by wood stoves. The results show that there is a clear relationship between pollution levels and occupancy intensity, and that the latter does not follow the schedules outlined in international standards or building codes. The results and methodology can be replicated in cities where air pollution is driven by wood stoves, allowing public authorities to have access to accurate occupancy schedules and providing them with reliable data to address local air pollution problems.

Data driven three-dimensional temperature and salinity anomaly reconstruction of the northwest Pacific Ocean

By virtue of the rapid development of ocean observation technologies, tens of petabytes of data archives have been recorded, among which, the largest portion are those derived from the orbital satellites, embodying the character of ocean surface. Nevertheless, the insufficiency of information below the subsurface restricts the utilization of these data and the understanding of ocean dynamics. To circumvent these difficulties, we present the spatially three-dimensional reconstruction of ocean hydrographic profiles at depth based on the satellites and in-situ measurement data. In this manuscript, long short-term memory network (LSTM) and Gaussian process regression (GPR) methods are invoked to predict the temperature and salinity profiles in the northwest Pacific Ocean, and to improve computational and storage efficiency, the proper orthogonal decomposition (POD) method is subtly incorporated into these two models. LSTM and GPR show satisfactory results, with the root mean square error (RMSE) of temperature is less than 1.45, and the RMSE of salinity is less than 0.19. The incorporation of the POD method substantially accelerates efficiency, particularly in the LSTM model, which improves 7.5-fold without significant accuracy loss. The sensitivity of different sea surface parameters on the reconstructed profiles reveals that sea surface height anomaly and latitude significantly influence the reconstruction of temperature anomaly (TA) and salinity anomaly (SA) profiles. Besides, sea surface salinity and sea surface temperature anomalies can improve the model's estimation ability for the upper TAs and SAs, respectively. The contribution of monthly climatology to temperature and salinity profile estimation is also explored in this paper. It is shown that adding monthly mean climatology to the input of the model can achieve more accurate estimates.

English

The study aims to make geometric improvements to State Highway 16 Aluva town to Kothamangalam, situated in the district of Ernakulam in Kerala, India. The length of the project road is 34.340km. The paper deals with literature review and data collection, the identification of geometric deficiencies, possible realignments, geometric improvements to alignment, vertical profile and cross-sections and estimation of costs.

Assessment of Cardiovascular Disease Risk in Females with Subclinical Hypothyroidism

Background. Subclinical hypothyroidism (SCH) is a common endocrine disorder prevalent in the Nepalese female population. Dyslipidemia, a prerequisite to the development of cardiovascular disease, links the thyroid profile and cardiovascular disease risk. This study is aimed at assessing the cardiovascular disease risk in females with SCH. Methods. This laboratory-based cross-sectional study was carried out at Manmohan Memorial Teaching Hospital, Kathmandu, Nepal, where 100 females with SCH and 100 euthyroid controls were included. Estimates of thyroid and lipid profiles were made, and lipid variables were used to calculate lipid indices. Results. In comparison to controls, females with SCH had significantly higher lipid profiles, thyroid profiles, and lipid indices but significantly lower HDL-C. The TSH ( p &lt; 0.001 ), TG ( p = 0.039 ), VLDL ( p = 0.039 ), and AIP ( p = 0.031 ) were significantly associated with mild and severe SCH. AIP was significantly correlated with TSH ( r = 0.256 , p = 0.010 ) among SCH females. Conclusion. Our findings suggest that women with SCH are more likely to get CVD. Hence, timely monitoring of cardiovascular status among females with SCH is crucial, and it can be performed using simple lipid indices like AIP, AI, and LCI.

Core scale investigation of fluid flow in the heterogeneous porous media based on X-ray computed tomography images: Upscaling and history matching approaches

In this paper, experiments and simulations were performed on outcrop samples from Lagoa Salgada in Rio de Janeiro, Brazil, as a possible analog to one of the most typical Brazilian Pre-salt carbonate reservoirs rocks. The rocks were microbial carbonates where plugs comprising two main facies were sampled, simplified as fine-grained and vugular facies. The plugs were utilized to study the impact of pore geometry with both experimental and simulation approaches on recovery factor, saturation profile, and relative permeability estimations. To provide direct visualization of the geometry, description of pore structure, and calculation of concentration profiles, computed tomography (CT) imaging was integrated with experimental measurements of petrography and core flooding. The injection of two pore volumes of formation water resulted in a recovery factor between 28 and 34 percent for the plug samples. Furthermore, based on porosity generated by dry and wet CT, as well as saturation profiles resulting from CT data collected during drainage and imbibition processes along the length of the plugs, it is revealed that the distribution of these properties was diverse and heterogeneous. An algorithm was used to process the 2D tomography images of the samples to remove the region related to the exterior parts. The images were then stacked to create a 3D fine-scale grid to simulate the porous media and the fluid flow by applying rules for the segmentation of rock types, porosity, and permeability estimations of each grid block. Course-scale grids were created by applying upscaling techniques to reduce computation time. Simulated produced fluid cuts for different upscaled models were compared with the experimental results from core flooding. A history-matching technique was then applied to match experimental and simulation results, calculating the relative permeability of two main defined facies and creating an updated model capable of assessing past and present performance and future forecasting. Since relative permeability is essential for accurate simulation, estimating these curves in the heterogeneous pre-salt reservoir considering different facies, greatly influences reasonable future prediction performance and the ability to make informed operational decisions.

Estimation of temperature-dependent growth profiles for the assessment of time of hatching in forensic entomology

Abstract Forensic entomology contributes important information to criminal investigations. This paper proposes a novel method to estimate the hatching time of fly larvae based on the temperature profile at the crime scene and on experimental data on larval development, where larvae are exposed to a constant temperature. We develop a dynamic model to estimate the growth curve under time-varying temperature profiles and the corresponding hatching time at the crime scene. Asymptotic properties are provided for the proposed estimators, and we explore their robustness via simulations. The proposed methodology is demonstrated on data from two criminal cases from the UK.

Pharmacophore modelling-based drug repurposing approaches for monkeypox therapeutics

Monkeypox is a zoonotic viral disease that mainly affects tropical rainforest regions of central and west Africa, with sporadic exportations to other places. Since there is no cure, treating monkeypox with an antiviral drug developed for smallpox is currently acceptable. Our study mainly focused on finding new therapeutics to target monkeypox from existing compounds or medications. It is a successful method for discovering or developing medicinal compounds with novel pharmacological or therapeutic applications. In this study, homology modelling developed the Monkeypox VarTMPK (IMNR) structure. Ligand-based pharmacophore was generated using the best docking pose of standard ticovirimat. Further, molecular docking analysis showed compounds, tetrahydroxycurcumin, procyanidin, rutin, vicenin-2, kaempferol 3-(6''-malonylglucoside) were the top five binding energy compounds against VarTMPK (1MNR). Furthermore, we carried out MD simulations for 100 ns for the six compounds, including reference based on the binding energies and interactions. MD studies revealed that as ticovirimat interacted with residues Lys17, Ser18, and Arg45, all the above five compounds interacted with the same amino acids at the active site during docking and simulation studies. Among all the compounds, ZINC4649679 (Tetrahydroxycurcumin) was shown to have the highest binding energy −9.7 kcal/mol and also observed stable protein-ligand complex during MD studies. ADMET profile estimation showed that the docked phytochemicals were safe. However, further biological assessment through a wet lab is essential to measure the efficacy and safety of the compounds. Communicated by Ramaswamy H. Sarma