Inverse Modeling Techniques Research Articles

Three-Dimensional Subsurface Model of Luk-Ulo Melange Complex, Karangsambung, Indonesia: Insights from Gravity Modeling

The Luk-Ulo Melange Complex (LMC) is characterized by a chaotic assemblage of mixed rocks with a block-in-matrix fabric. The exposed blocks consist of various scattered rock types, trending in an ENE-WSW direction. In the case of Mt. Parang, the origin of the diabase remains uncertain, with ongoing debate as to whether it is associated with in situ volcanic activity or represents an exotic block within the melange deposit. Subsurface data obtained through geophysical investigation can aid in modeling the geometry of intrusive bodies using inverse modeling techniques. In this study, we conducted a gravity survey and performed 3D inverse modeling to investigate the subsurface beneath Karangsambung. A total of 818 gravity data points and 28 rock density measurements were integrated with existing geological data to construct an a priori 3D geological model. To ensure the results align with geological concepts, the 3D inversion utilized a stochastic approach, allowing for the incorporation of multiple geological constraints over fifty million iterative procedures. Ultimately, the inversion successfully reduced the misfit between observed and calculated data from 2.71 to 0.55 mGal. Based on the inverted 3D model, the diabase rock in Mt. Parang is identified as having an intrusive origin. The intrusion model exhibited minimal changes in density, volume, and shape during the inversion process. Additionally, the model suggests the presence of a solidified magma reservoir at a depth of approximately 3 km, potentially related to Dakah volcanism. The inverted model also reveals the block-in-matrix structure of the Luk-Ulo Melange Complex in the northern area.

Jump around: Selecting Markov Chain Monte Carlo parameters and diagnostics for improved food web model quality and ecosystem representation

Capturing ecological data variability in food web models is an important step for improving model representation of empirical systems. One approach is to use linear inverse modelling and Markov Chain Monte Carlo (LIM-MCMC) techniques to set up an inverse LIM problem using empirical data constraints, and then sample multiple plausible food webs from the inverse problem using an MCMC algorithm. We describe the set of plausible food webs as an ‘ensemble’ of solutions to the inverse problem sampled with the LIM-MCMC algorithm. The extent of data variability eventually integrated into an ensemble depends on how well the LIM-MCMC algorithm samples the solution space. Algorithm quality can be adjusted via user-defined parameters describing starting points, jump sizes, and number of iterations or food webs produced. However, little information exists on how each LIM-MCMC algorithm parameter affects the degree of empirical data variability introduced into the ensemble. Further, post hoc algorithm quality diagnostics with commonly used trace plots and the coefficient of variation (CoV) rarely address critical aspects of algorithm quality, such as (1) if the returned ensemble successfully targeted the solution space distribution (stationarity), (2) correlation between ensemble solutions (mixing), and (3) if the ensemble contains enough solutions to adequately capture input data variability (sampling efficiency). Therefore, we used several established MCMC convergence diagnostics to (1) quantify how algorithm parameters affect ensemble flow values and if these differences propagate to ecological indicators and (2) evaluate algorithm quality and compare to current evaluation and ecosystem modelling methods. We applied 30 LIM-MCMC algorithm combinations of varying starting points, jump sizes, and number of iterations to solve food web ensembles from a single food web model. We analysed ensembles with Ecological Network Analysis (ENA) to calculate indicators describing system function. Results show that LIM-MCMC algorithm parameters, in particular the jump size, affect ensemble flow values, which propagate to ecological indicators describing different ecosystem function of the same model. Thereafter, comparisons of post hoc diagnostics show that MCMC convergence diagnostics provided more robust estimates of algorithm quality than trace plots and CoV. Together, these findings underpin several novel recommendations to enhance LIM-MCMC algorithm parameter selection and quality assessments applicable to any ecological ensemble network study.

Bayesian inversion for in-situ thermal characterisation of walls in the presence of thermal anomalies

This paper develops a Bayesian inverse modelling framework for the in-situ characterisation of walls' thermal performance in the presence of thermal anomalies subject to uncertainty. For any given wall, the proposed framework uses in-situ contact measurements of temperature and heat flux in order to approximate the (posterior) distribution of a set of parameters which are inputs of a 3D heat transfer model of the wall that accounts for the presence of unknown anomalies. The set of inputs that we infer include (i) the geometry of the thermal anomaly as well was its material properties, (ii) the as-built thermophysical properties of the clear part (i.e. without anomaly) of the wall (iii) surface resistances and (iv) modelling errors that arise from unaccounted sources of uncertainty in the boundary conditions. The geometry of the unknown thermal anomaly is parameterised with a level-set function that is inferred within the proposed Bayesian approach. In order to incorporate prior statistical information of the thermal anomaly, a Gaussian process conditioned on measurements from thermal images is employed to build a prior on the level-set function. The posterior distribution of inferred inputs is approximated via the implementation of an Ensemble Kalman Inversion algorithm that provides stable and accurate approximations of the posterior. This paper also proposes a methodology that uses the inferred 3D model to derive a thermally equivalent 1D model of the wall that is suitable for its integration with existing building energy performance software. The proposed inverse modelling technique is experimentally validated by characterising the thermal performance of an insulation panel in the presence of a thermal anomaly. Our computations show that the proposed approach produces a 3D model that accurately describes thermal performance, and a derived equivalent 1D model that effectively reproduces the dynamic thermal performance of the insulation panel in the presence of the thermal anomaly.

Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning

Geopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC (Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With various supplementary cementitious materials, the compressive strength of geopolymer concrete should be accurately predicted. Recent studies have applied deep learning techniques to predict the compressive strength of geopolymer concrete yet its hidden decision-making criteria diminish the end-users’ trust in predictions. To bridge this gap, the authors first developed three deep learning models: an artificial neural network (ANN), a deep neural network (DNN), and a 1D convolution neural network (CNN) to predict the compressive strength of slag ash-based geopolymer concrete. The performance indices for accuracy revealed that the DNN model outperforms the other two models. Subsequently, Shapley additive explanations (SHAP) were used to explain the best-performed deep learning model, DNN, and its compressive strength predictions. SHAP exhibited how the importance of each feature and its relationship contributes to the compressive strength prediction of the DNN model. Finally, the authors developed a novel DNN-based open-source software interface to predict the mix design proportions for a given target compressive strength (using inverse modeling technique) for slag ash-based geopolymer concrete. Additionally, the software calculates the Global Warming Potential (kg CO2 equivalent) for each mix design to select the mix designs with low greenhouse emissions.

Improved energy retrofit decision making through enhanced bottom-up building stock modelling

Modelling the performance of building stocks is crucial in facilitating the renovation at the building stock level. Bottom-up building stock modelling begins by detailing individual buildings and then aggregates them into stock level. Its primary advantage lies in capturing the inherent heterogeneity among distinct buildings, which enables tailored retrofitting. Naturally, this approach requires a comprehensive dataset with detailed building information such as geometry and envelope thermal properties. However, a common challenge is the incompleteness of available data in individual datasets. To address this, previous bottom-up studies have filled the missing data with representative or statistical data. Such practice could lead to homogeneous modelling of distinctbuildings within the same statistical group. This limits the utilization of key ability of bottom-up building stock modelling in capturing heterogeneity, such as tailored retrofitting to explore potential retrofitting areas and strategies.To address this challenge of homogeneous modelling, we utilize data fusion framework for bottom-up building stock modelling, employing probabilistic record linkage and inverse modelling techniques to integrate multiple incomplete building performance datasets. This framework fills the missing data in one dataset with information from another, thus capturing inherent heterogeneity in the building stock. An empirical study was conducted in Umeå, Sweden, to investigate the framework’s effectiveness by modelling building stock with various retrofitting strategies. This study contribution lies in enhancing bottom-up building stock modelling by capturing inherent heterogeneity, to provide tailored retrofitting solutions.

Breast Cancer Screening Using Inverse Modeling of Surface Temperatures and Steady-State Thermal Imaging.

Cancer is characterized by increased metabolic activity and vascularity, leading to temperature changes in cancerous tissues compared to normal cells. This study focused on patients with abnormal mammogram findings or a clinical suspicion of breast cancer, exclusively those confirmed by biopsy. Utilizing an ultra-high sensitivity thermal camera and prone patient positioning, we measured surface temperatures integrated with an inverse modeling technique based on heat transfer principles to predict malignant breast lesions. Involving 25 breast tumors, our technique accurately predicted all tumors, with maximum errors below 5 mm in size and less than 1 cm in tumor location. Predictive efficacy was unaffected by tumor size, location, or breast density, with no aberrant predictions in the contralateral normal breast. Infrared temperature profiles and inverse modeling using both techniques successfully predicted breast cancer, highlighting its potential in breast cancer screening.

Core loss determination in electric machines using short‐time transient thermal measurements

AbstractThis paper introduces a novel application of the inverse modelling method, which is designed to estimate core losses in both the stator and rotor regions of an electrical machine. The technique focuses on the use of short‐term transient temperature measurements obtained from the stator core of a slotless induction machine, with a focus on validating the measured temperature rise through a forward model. The measurement setup involves two primary approaches: (i) the use of thermal sensors embedded in a printed circuit board inside the stator core and (ii) surface sensors embedded on the stator yoke. Through the use of this innovative approach, the results indicate that the inverse modelling technique is highly effective in predicting core losses based on short‐time transient temperature rise measurements.

Inverse Modelling Techniques as Dynamic Digital Pre-Distortion for Broadband Systems for CS-DACs Based on Volterra Series Expansion

Inverse Modelling Techniques as Dynamic Digital Pre-Distortion for Broadband Systems for CS-DACs Based on Volterra Series Expansion

A Review of Techniques and Bio-Heat Transfer Models Supporting Infrared Thermal Imaging for Diagnosis of Malignancy

The present review aims to analyze the application of infrared thermal imaging, aided by bio-heat models, as a tool for the diagnosis of skin and breast cancers. The state of the art of the related technical procedures, bio-heat transfer modeling, and thermogram post-processing methods is comprehensively reviewed. Once the thermal signatures of different malignant diseases are described, the updated thermographic techniques (steady-state and dynamic) used for cancer diagnosis are discussed in detail, along with the recommended best practices to ensure the most significant thermal contrast observable between the cancerous and healthy tissues. Regarding the dynamic techniques, particular emphasis is placed on innovative methods, such as lock-in thermography, thermal wave imaging, and rotational breast thermography. Forward and inverse modeling techniques for the bio-heat transfer in skin and breast tissues, supporting the thermographic examination and providing accurate data for training artificial intelligence (AI) algorithms, are reported with a special focus on real breast geometry-based 3D models. In terms of inverse techniques, different data processing algorithms to retrieve thermophysical parameters and growth features of tumor lesions are mentioned. Post-processing of infrared images is also described, citing both conventional processing procedures and applications of AI algorithms for tumor detection.

Enhancing understanding of moisture diffusion in wood: numerical approach and diffusion parameter optimization

Wood, an environmentally friendly construction material, is characterized by a critical attribute that significantly influences its mechanical properties and durability: its moisture absorption behavior. This study expands upon an existing one-dimensional model to develop an innovative two-dimensional numerical framework for simulating moisture propagation within wood. Utilizing the finite difference method, this approach offers a more detailed analysis of moisture behavior in wood structures. The research adopts an inverse modeling technique, integrating a simplex optimization algorithm programmed in VBA software. This algorithm is employed to deduce diffusion parameters from the evolution of moisture content over time. Focusing on the analysis of moisture diffusion parameters, the study examines both longitudinal and transverse directions in two temperate species (Beech and Fir) and two tropical species (Moabi and Ozigo). The findings provide insightful data on the hygroscopic behavior of these woods, revealing significant distinctions between temperate and tropical species. This research offers valuable information for the application of these wood species in construction and other fields, enhancing the understanding of their moisture-related properties.

Seasonal ammonia emissions from an intensive beef cattle feedlot in Victoria Australia

Ammonia (NH3) emitted from concentrated animal feeding operations can cause environmental and health problems, and indirectly contribute to greenhouse gas emissions. Cattle feedlots are known to be large sources of NH3, but few studies have documented seasonal emissions from Australian feedlots. We conducted two field campaigns to measure NH3 emissions from an intensive beef cattle feedlot in southeast Australia, and these results were combined with previous measurements at the same feedlot to document seasonal variations in emissions and to derive annual feedlot emission factors (EFs). Emission rates were calculated with an inverse dispersion modelling (IDM) technique, based on NH3 concentrations measured at the feedlot with open-path lasers (OPLs). The average area emission rates in spring, summer, autumn and winter were 90.5, 167.4, 96.2 and 86.8 μg NH3 m−2 s−1 from the cattle pens, and 22.5, 18.1, 7.7 and 20.7 μg NH3 m−2 s−1 from the manure stockpile area, respectively. The total per-animal EFs ranged from 126.0 (autumn) to 190.2 g NH3 animal−1 d−1 (summer), representing a loss of 47.5–64.6% of the fed N. Seasonal variations in emissions were related to air temperature. Slight changes in crude protein content of the cattle diet may also have impacted seasonal variability. Taking seasonal variations into consideration, the average feedlot EF was 160.4 g NH3 animal−1 d−1, with 90% of the emissions coming from the cattle pens. Extrapolating the EF to all feedlot cattle in the country, the direct NH3 emissions from Australian feedlots amount to 65.2 Gg NH3 annually, or 3.7% of the national total. Our study benchmarks seasonal and annual EFs and N losses for Australian commercial feedlots, and provides a baseline for extrapolating the impacts of mitigation efforts.

The relative contribution of mantle and continental crustal sources to primitive arc magmas: Insights from the early Palaeozoic Famatinian - Puna arc

Subduction zone magmatism plays a crucial role in driving the exchange of chemical elements between Earth's interior and surface. This survey focuses on the compositional analysis of primitive mafic rocks within an ancient Early Palaeozoic subduction-related magmatic belt. The primary objective is to assess the petrological processes involved in the cycling of chemical elements from the Earth's surface, through the slab and mantle reservoirs, to the crustal arc magmatic systems. By examining the composition of primitive mafic rocks and utilizing inverse modelling techniques, we estimate the composition of the ambient mantle source, which appears to have undergone depletion due to melt extraction before being metasomatized by slab agents. The inverse approach reveals that fluid-mobile elements (Th, U, La, Ce, and Sr) were primarily derived from the slab contribution and suggests that subduction had influenced the abundances of Nb and Ta in the modified mantle source. Conversely, Zr, Hf, and Ti were sourced from the ambient mantle. However, the available mineral/melt partition coefficients used in inverse modelling may overestimate the contribution of Sr and Nb from the slab. Instead, forward modelling is limited by the incomplete preservation of ancient volcanic arc geology. The forward mixing models that consider the interaction between slab-released sedimentary-derived melts and the ambient depleted mantle can explain the observed variations in Nd and Sr isotopic ratios in primitive mafic rocks. Two-component mixing shows that a small mass fraction (≤1.5 wt%) of average Cambrian continental sediments subducted beneath the Early Ordovician arc into a MORB-depleted mantle source account for the abundance of sedimentary-supplied elements such as Th, La, and Ba, as well as radiogenic isotope ratios of 143Nd/144N and 87Sr/86Sr in the primitive mafic magmas. The chemical and isotopic composition of deep-seated primitive mafic rocks reveals a decoupling between incompatible trace elements, which display minimal variation, and radiogenic isotopic ratios, which exhibit a wide range and reflect contributions from continental crustal reservoirs. To explain the observed variations in trace elements and isotopes, we propose a simple model involving simultaneous bulk assimilation (A) and imperfect fractional crystallization (IFC) in an open igneous system, where crystal accumulation and assimilation occur concurrently. Especially, deep crustal physicochemical interaction of primitive arc magmas with supracrustal host rocks may mask the expected clear signature of subarc mantle petrological processes. Our assessment of the Famatinian-Puna arc, an ancient subduction zone, concurs with estimates from active volcanic arcs, underscoring the general applicability of this study case to subduction zone magmatism.

Implementation of a satellite-based tool for the quantification of CH4 emissions over Europe (AUMIA v1.0) – Part 1: forward modelling evaluation against near-surface and satellite data

Abstract. Methane is the second-most important greenhouse gas after carbon dioxide and accounts for around 10 % of total European Union greenhouse gas emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modelling techniques appear as powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modelling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modelling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modelling component will be based on the WRF model coupled to a Lagrangian particle dispersion module. A description of the modelling tools, input data sets, and 1-year forward modelling evaluation from 1 April 2018 to 31 March 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modelling for Denmark, will be provided in a second paper. A good general agreement is found between the modelling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model–observation discrepancies for the summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from 1 % to 2 %. Domain-wide correlation coefficients and root-mean-square errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. On the other hand, model–observation discrepancies for winter months show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 % to 3 %. Domain-wide correlation coefficients and root-mean-square errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modelling results, the methane enhancement above the background concentrations came almost entirely from anthropogenic sources; however, these sources contributed with only up to 2 % to the methane total-column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe and demonstrate a huge potential for methane inverse modelling using improved TROPOMI products in large-scale applications.

An empirical firebrand pile heat flux model

Firebrand exposure has been identified as a leading cause of structure losses during wildfires. However, only a limited number of studies have attempted to quantify the incident firebrand heat flux to a target substrate surface. In this study, a bench-scale wind tunnel retrofitted with an IR thermal imaging system was used to generate high-resolution temperature data for the back surface of a thin, non-combustible insulation board exposed to a glowing firebrand pile at 0.9–2.7 m s−1 air flow velocities and 0.06 and 0.16 g cm−2 firebrand coverage densities. An inverse heat flux modelling technique utilizing a solid-phase pyrolysis solver, ThermaKin, was employed to determine transient firebrand pile heat flux profiles underneath and directly in front of a glowing firebrand pile. The inverse modelling technique used both the experimental back surface temperature data and the insulation's thermophysical properties. Average incident firebrand pile heat flux values obtained for all testing conditions ranged between 28 and 80 kW m−2. For the first time, an empirical model capturing the firebrand pile incident heat flux dependence on time, air flow velocity and firebrand coverage density was developed.

Enabling fire source localization in building fire emergencies with a machine learning-based inverse modeling approach

During building fire emergency response (ER), one of the most important tasks for firefighting is to locate the fire source. Currently, incident commanders on the fire ground determine the origin of fire sources, relying mostly on the reports of firefighters entering the building or the information from on-site witnesses. This inevitably requires firefighters to approach the fire origin and observe, causing significant dangers and risks. To overcome this issue, an inverse model for building fire source localization is developed. A machine learning (ML)-based inverse modeling approach is used to build the complex relationship between the fire source location and on-site temperature sensor measurements, so that an inversion of the fire source based on the temperature observations can be enabled. By taking fire in an actual building floor as a case study, the inverse model for fire source localization using simulated fire temperature data is established. ML methods of feed-forward neural networks (FFNNs) and long short-term memory (LSTM) are respectively employed. Results indicate that LSTM in stateless mode is more appropriate to establish the relative models because more accurate recognition of the fire source room can be achieved during the initial and growth stages of fire development. Besides, by considering different means of on-site data acquisition in building fire emergencies, fire source localization via stationary temperature sensor arrays and portable temperature measuring devices is realized. The formulated models achieve recognition accuracy of 99.5% (direct recognition) and 92.1% (in top-3 candidates) in the first 15 min of the fires, respectively. The effectiveness of using the ML-based inverse modeling technique for fire source localization in building fire emergencies is demonstrated.

Machine learning based approach to predict ductile damage model parameters for polycrystalline metals

Damage models for ductile materials typically need to be parameterized, often with the appropriate parameters changing for a given material depending on the loading conditions. This can make parameterizing these models computationally expensive, since an inverse problem must be solved for each loading condition. Using standard inverse modeling techniques typically requires hundreds or thousands of high-fidelity computer simulations to estimate the optimal parameters. Additionally, the time of a human expert is required to set up the inverse model. Machine learning has recently emerged as an alternative approach to inverse modeling in these settings, where the machine learning model is trained in an offline manner and new parameters can be quickly generated on the fly, after training is complete. This work utilizes such a workflow to enable the rapid parameterization of a ductile damage model called TEPLA with a machine learning inverse model. The machine learning model can efficiently estimate the model parameters much faster, as compared to previously employed methods, such as Bayesian calibration. The results demonstrate good accuracy on a synthetic test dataset and is validated against experimental data.

Bayesian approach to a generalized inherent optical property model.

Relationships between the absorption and backscattering coefficients of marine optical constituents and ocean color, or remote sensing reflectances Rrs(λ), can be used to predict the concentrations of these constituents in the upper water column. Standard inverse modeling techniques that minimize error between the modeled and observed Rrs(λ) break down when the number of products retrieved becomes similar to, or greater than, the number of different ocean color wavelengths measured. Furthermore, most conventional ocean reflectance inversion approaches, such as the default configuration of NASA's Generalized Inherent Optical Properties algorithm framework (GIOP-DC), require a priori definitions of absorption and backscattering spectral shapes. A Bayesian approach to GIOP is implemented here to address these limitations, where the retrieval algorithm minimizes both the error in retrieved ocean color and the deviation from prior knowledge, calculated using output from a mixture of empirically-derived and best-fit values. The Bayesian approach offers potential to produce an expanded range of parameters related to the spectral shape of absorption and backscattering spectra.

Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy

Abstract. In response to the need for an up-to-date emissions inventory and the recent achievement of geostationary observations afforded by the Geostationary Environment Monitoring Spectrometer (GEMS) and its sister instruments, this study aims to establish a top-down approach for adjusting aerosol precursor emissions over East Asia. This study involves a series of the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product, the GEMS aerosol optical depth (AOD) data fusion product and its proxy product, and chemical transport model (CTM)-based inverse modeling techniques. We begin by sequentially adjusting bottom-up estimates of nitrogen oxides (NOx) and primary particulate matter (PM) emissions, both of which significantly contribute to aerosol loadings over East Asia to reduce model biases in AOD simulations during the year 2019. While the model initially underestimates AOD by 50.73 % on average, the sequential emissions adjustments that led to overall increases in the amounts of NOx emissions by 122.79 % and of primary PM emissions by 76.68 % and 114.63 % (single- and multiple-instrument-derived emissions adjustments, respectively) reduce the extents of AOD underestimation to 33.84 % and 19.60 %, respectively. We consider the outperformance of the model using the emissions constrained by the data fusion product to be the result of the improvement in the quantity of available data. Taking advantage of the data fusion product, we perform sequential emissions adjustments during the spring of 2022, the period during which the substantial reductions in anthropogenic emissions took place accompanied by the COVID-19 pandemic lockdowns over highly industrialized and urbanized regions in China. While the model initially overestimates surface PM2.5 concentrations by 47.58 % and 20.60 % in the North China Plain (NCP) region and South Korea (hereafter referred to as Korea), the sequential emissions adjustments that led to overall decreases in NOx and primary PM emissions by 7.84 % and 9.03 %, respectively, substantially reduce the extents of PM2.5 underestimation to 19.58 % and 6.81 %, respectively. These findings indicate that the series of emissions adjustments, supported by the TROPOMI and GEMS-involved data fusion products, performed in this study are generally effective at reducing model biases in simulations of aerosol loading over East Asia; in particular, the model performance tends to improve to a greater extent on the condition that spatiotemporally more continuous and frequent observational references are used to capture variations in bottom-up estimates of emissions. In addition to reconfirming the close association between aerosol precursor emissions and AOD as well as surface PM2.5 concentrations, the findings of this study could provide a useful basis for how to most effectively exploit multisource top-down information for capturing highly varying anthropogenic emissions.

Evaluation of soil water losses under irrigation saving techniques in a semi-arid region in Tunisia

Abstract In arid and semi-arid regions, managing agricultural water for irrigation is essential to cope with water scarcity and maximize crop yields. In this study, an experiment was conducted on a potato crop in the Manouba region (lower valley of Medjerda, Tunisia). The experimental protocol consisted of four water treatments utilizing water-saving irrigation techniques: FI (Full Irrigation 100%): irrigation with 100% of crop water requirements. DI (Irrigation 75%): deficit irrigation with the application of 75% of crop water requirements. PRDRight (Irrigation 50% on the right side): Irrigation by partial root drying. PRDLeft (Irrigation 50% on the left side): Irrigation by partial root drying. Simulation of soil water profiles was carried out by the Hydrus-1D model. The soil hydraulic properties were calibrated according to the experimental conditions using an inverse modeling technique. According to the obtained results, simulated soil water profiles were close to those measured. Indeed, the calculated NRMSE values are low, indicating the reliability of Hydrus-1D as a decision support tool to optimize water irrigation management. These results were then used to investigate the effects of a 2 °C temperature increase on soil water loss, and it was determined that the impact was insignificant.

A hybrid deep learning approach for the design of 2D low porosity auxetic metamaterials

Due to the remarkable ability of Deep learning (DL) to abstract hidden information, it has been proven to be a powerful tool in many tasks related to the design of metamaterials. DL-aided design techniques can be generally categorized into two types, including forward designs, which are training surrogate models to accelerate the simulation process, and inverse design, which uses inverse modeling techniques to generate the design that satisfies the corresponding requirement. Although generative models have a unique capability to generate multiple designs instantly with random information, they often underperform in accuracy compared to designs based on optimization techniques. In this paper, a hybrid design framework combining the advantages of both DL forward design and the inverse design based on the mixture density network (MDN) is proposed. Then the proposed framework is implemented for the inverse design of S-shaped perforated auxetic metamaterial. The hybrid design framework inherited the one-to-many mapping capability of MDN and has great capability of generating designs with designated mechanical properties at less than 10% relative errors, in most design scenarios (over 95% in the test set), at one to two orders of magnitude less computational cost compared to optimization-based forward design.