Future Models Research Articles

Simulating dust emissions and secondary organic aerosol formation over northern Africa during the mid-Holocene Green Sahara period

Abstract. Paleo-proxy data indicate that a “Green Sahara” thrived in northern Africa during the early- to mid-Holocene (MH; 11 000 to 5000 years before present), characterized by more vegetation cover and reduced dust emissions. Utilizing a state-of-the-art atmospheric chemical transport model, TM5-MP, we assessed the changes in biogenic volatile organic compound (BVOC) emissions, dust emissions and secondary organic aerosol (SOA) concentrations in northern Africa during this period relative to the pre-industrial (PI) period. Our simulations show that dust emissions reduced from 280.6 Tg a−1 in the PI to 26.8 Tg a−1 in the MH, agreeing with indications from eight marine sediment records in the Atlantic Ocean. The northward expansion in northern Africa resulted in an increase in annual emissions of isoprene and monoterpenes during the MH, around 4.3 and 3.5 times higher than that in the PI period, respectively, causing a 1.9-times increase in the SOA surface concentration. Concurrently, enhanced BVOC emissions consumed more hydroxyl radical (OH), resulting in less sulfate formation. This effect counteracted the enhanced SOA surface concentration, altogether leading to a 17 % increase in the cloud condensation nuclei at 0.2 % super saturation over northern Africa. Our simulations provide consistent emission datasets of BVOCs, dust and the SOA formation aligned with the northward shift of vegetation during the “Green Sahara” period, which could serve as a benchmark for MH aerosol input in future Earth system model simulation experiments.

Agricultural price forecasting based on the spatial and temporal influences factors under spillover effects

PurposeIntegrating artificial intelligence and quantitative investment has given birth to various agricultural futures price prediction models suitable for nonlinear and non-stationary data. However, traditional models have limitations in testing the spatial transmission relationship in time series, and the actual prediction effect is restricted by the inability to obtain the prices of other variable factors in the future.Design/methodology/approachTo explore the impact of spatiotemporal factors on agricultural prices and achieve the best prediction effect, the authors innovatively propose a price prediction method for China's soybean and palm oil futures prices. First, an improved Granger Causality Test was adopted to explore the spatial transmission relationship in the data; second, the Seasonal and Trend decomposition using Loess model (STL) was employed to decompose the price; then, the Apriori algorithm was applied to test the time spillover effect between data, and CRITIC was used to extract essential features; finally, the N-Beats model was selected as the prediction model for futures prices.FindingsUsing the Apriori and STL algorithms, the authors found a spillover effect in agricultural prices, and past trends and seasonal data will impact future prices. Using the improved Granger causality test method to analyze the unidirectional causality relationship between the prices, the authors obtained a spatial effect among the agricultural product prices. By comparison, the N-Beats model based on the spatiotemporal factors shows excellent prediction effects on different prices.Originality/valueThis paper addressed the problem that traditional models can only predict the current prices of different agricultural products on the same date, and traditional spatial models cannot test the characteristics of time series. This result is beneficial to the sustainable development of agriculture and provides necessary numerical and technical support to ensure national agricultural security.

Assessment of Arctic sea ice and surface climate conditions in nine CMIP6 climate models

ABSTRACT The observed retreat and anticipated further decline in Arctic sea ice holds strong climate, environmental, and societal implications. In predicting climate evolution, ensembles of coupled climate models have demonstrated appreciable accuracy in simulating sea-ice area trends throughout the historical period, yet individual climate models still show significant differences in accurately representing the sea-ice thickness distribution. To better understand individual model performance in sea-ice simulation, nine climate models were evaluated in comparison with Arctic satellite and reanalysis-derived sea-ice thickness data, sea-ice area records, and atmospheric reanalysis data of surface wind and air temperature. This assessment found that the simulated spatial distribution of historical sea-ice thickness varies greatly between models and that several key limitations persist among models. Primarily, most models do not capture the thickest regimes of multiyear ice present in the Wandel and Lincoln seas; those that do often possess erroneous positive bias in other regions such as the Laptev Sea or along the Eurasian Arctic Shelf. This analysis provides enhanced understanding of individual model historical simulation performance, which is critical in informing the selection of coupled climate model projections for dependent future modeling efforts.

Digital polycrystalline microstructure generation using diffusion probabilistic models

Accurate micromechanical simulation of polycrystalline materials requires a realistic digital representation of the grain scale microstructure. This work demonstrates the use of a generative diffusion probabilistic model for synthesizing single phase polycrystalline realizations. The model performs well and is capable of producing realistic microstructures consisting of not just simple equiaxed structures but also structures exhibiting more complex spatial arrangements. Masked microstructure generation reveals that the model is context aware of morphological descriptors which may be encoded in the latent space. Training on more diverse data sets, with scaled up architectures, may enable development of future models capable of synthesizing even more complex microstructural features.

Healthy lives and well-being for all at all ages: expanding representations of determinants of health within systems dynamics and integrated assessment models

Integrated Assessment Models (IAMs) and System Dynamic Models (SDMs) are starting to incorporate representations of the impact of environmental changes on health and socio-economic development into their modelling frameworks. We use this brief review to provide an overview of how health and well-being are currently represented in IAMs and SDMs. A grey literature search on 12 selected model host websites and their corresponding Wiki pages was conducted. Model descriptions, coverage and publications were then tabulated. Additional potential determinants related to health were then suggested based on emerging environmental health literature. Based on these tabulations, it was determined that many individual health outcomes are not represented and thus not analyzed. Social well-being is not represented at all. Additionally, potentially health relevant determinants such as chemical or metal exposure and water pollution are rarely represented in models. Most models have representations of climate, outdoor air pollution and food availability. Air pollution was the most analyzed determinant, especially in relation to its respiratory effects. We suggest that future modelers incorporate more representations of environmental determinants influencing health, and to analyze all available determinants in relation to a wider array of health outcomes. Perhaps, if and when broader determinants of health are represented in IAMs and SDMs, then a composite of these determinants could be used to determine a population’s ability to achieve elements that also contribute to social well-being.

Experimental observation of the vertical displacement between heating and levitation regions in an electromagnetic levitation coil

Scientific progress in the relevant fields of science and technology requires the production of crystals with quality beyond the current state of the art. Electro-magnetic levitation (EML) is a prospective method for the growth of high-purity crystals, allowing for avoidance of any contact between the crystal-melt and the crucible. Contactless crystal growth reduces the number of crystal defects commonly abundant in conventional crystal growth methods. The EML method also allows crystal growth of materials with very high melting points. In this article, we report detailed measurements of the EML method. The induction coil used in this study has three turns and one counterturn. We subject different metal material (Al, Cu, Sn, and Ni) samples to the induction coil’s electromagnetic field. For each sample, we measure the induced lift force, Joule heating, and components of magnetic induction as a function of position inside the coil. The results show that the maximum heating in an EML coil is emitted in the area below the levitation zone, a discrepancy not reported earlier. Our findings suggest that this shift should be considered in coil design to avoid instability of the levitated material. We hope this study will serve as a stepping stone for developing EML techniques. The experimental results we provide will be used to evaluate the accuracy of current and future theoretical models of EML coils. This, in turn, will facilitate progress in the application of EML to the growth of larger crystals of higher quality.

A model of programmatic co-design with teachers: five factors for success

ABSTRACT This paper summarises the co-design model utilised throughout 2020–2022 by WeatherBlur, a community-based citizen science project. Project leaders and teachers working in classrooms across multiple states collaborated to develop iterative instructional practices for classroom implementation and professional development to support teachers’ use of the program. Participants received necessary support and training to facilitate their ongoing success as the project evolved and grew. External evaluators tracked the planning group’s co-design process, collecting data on the research-practitioner partnership and the ways their input impacted the project’s development over time. During the final year of the project, the planning group reflected upon their work and identified five criteria that emerged as successful elements of this co-design process. 1. Creating a culture of trust, 2. Time and patience, 3. Foundational knowledge and deconstruction for understanding, 4. Mutually beneficial collaboration, and 5. Commitment to engagement and flexibility. We present a full explanation of these five criteria, including how the WeatherBlur team developed and nurtured the associated behaviours and strategies. This set of takeaways is applicable to many contexts, and this paper provides insight for future co-design models seeking to replicate a development process that utilises collective resources and input from a range of collaborators.

Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set.

Machine learning (ML) methods have shown promise for discovering novel catalysts but are often restricted to specific chemical domains. Generalizable ML models require large and diverse training data sets, which exist for heterogeneous catalysis but not for homogeneous catalysis. The tmQM data set, which contains properties of 86,665 transition metal complexes calculated at the TPSSh/def2-SVP level of density functional theory (DFT), provided a promising training data set for homogeneous catalyst systems. However, we find that ML models trained on tmQM consistently underpredict the energies of a chemically distinct subset of the data. To address this, we present the tmQM_wB97MV data set, which filters out several structures in tmQM found to be missing hydrogens and recomputes the energies of all other structures at the ωB97M-V/def2-SVPD level of DFT. ML models trained on tmQM_wB97MV show no pattern of consistently incorrect predictions and much lower errors than those trained on tmQM. The ML models tested on tmQM_wB97MV were, from best to worst, GemNet-T > PaiNN ≈ SpinConv > SchNet. Performance consistently improves when using only neutral structures instead of the entire data set. However, while models saturate with only neutral structures, more data continue to improve the models when including charged species, indicating the importance of accurately capturing a range of oxidation states in future data generation and model development. Furthermore, a fine-tuning approach in which weights were initialized from models trained on OC20 led to drastic improvements in model performance, indicating transferability between ML strategies of heterogeneous and homogeneous systems.

Regridding uncertainty for statistical downscaling of solar radiation

Abstract. Initial steps in statistical downscaling involve being able to compare observed data from regional climate models (RCMs). This prediction requires (1) regridding RCM outputs from their native grids and at differing spatial resolutions to a common grid in order to be comparable to observed data and (2) bias correcting RCM data, for example via quantile mapping, for future modeling and analysis. The uncertainty associated with (1) is not always considered for downstream operations in (2). This work examines this uncertainty, which is not often made available to the user of a regridded data product. This analysis is applied to RCM solar radiation data from the NA-CORDEX (North American Coordinated Regional Climate Downscaling Experiment) data archive and observed data from the National Solar Radiation Database housed at the National Renewable Energy Lab. A case study of the mentioned methods over California is presented.

The effect of size and shape of annular centrifugal contactors upon hydrodynamics and mass transfer using HNO3-TBP/OK

Annular centrifugal contactors (ACCs) are process intensification equipment for liquid-liquid extraction (also known as solvent extraction) that use centrifugal force that 200x greater than traditional gravity settling (Baker et al., 2022a). The size and throughput of ACCs have been denoted by their increasing rotor diameter (RD), from 10 mm up to 1330 mm. In this work a multiscale ACC platform has been commissioned, (consisting of 10, 25, 40 mm RD ACCs), to collect the physical liquid-holdup and mass transfer across several sizes to inform future computation modelling flowsheets of liquid-liquid extraction (LLE).Experimental studies have been performed with nitric acid with tributyl phosphate in odourless kerosene (TBP/OK) relevant to the development of advanced PUREX processes for recovery of actinides from spent fuel. Hydrodynamic trials determined the minimum Relative Centrifugal Force (RCF) required to separate extreme solvent/aqueous (S/A) feed ratios and total throughputs across the three sizes of ACC. The novel 'stepped stop' method has provided understanding of the actual liquid holdups within the two distinctly different regions (mixing annulus and separating rotor) ACC during operation to provide accurate residence times. These values were subsequently used to calculate the mass transfer and stage efficiency of aqueous nitric acid with tributyl phosphate in odourless kerosene. The smallest contactor was determined to be distinct and likely under-predicting performance of larger contactors. Ultimately the geometry and the aspect ratio supersedes using rotor diameter for defining scale-up of annular centrifugal contactors.

Exploring germination requirements of Iva xanthiifolia: Implications for potential spread in Europe

Iva xanthiifolia, an invasive annual weed species native to North American prairies, is an agricultural problem in different parts of the world and represents a health hazard as an allergen to humans. This study addresses the limited literature on its germination requirements, focusing on base temperature, base water potential, and salinity tolerance. Seeds from a Serbian population near Belgrade were tested to various conditions. The experiments were conducted using temperatures ranging from 1 °C to 30 °C and water potentials ranging from 0 to –2 MPa, while salinity tolerance was tested at three temperatures (1, 3, and 6 °C) and five salinity levels (0 to 16 dS/m). Results indicate higher germination rates at lower temperatures (1 °C) compared to higher temperatures (30 °C), with a base temperature of 0.7 ± 0.37 °C. Germination decreased with increased water potential, showing a base water potential of -1.58 ± 0.07 MPa. Salinity tolerance was observed, with germination even at 16 dS/m (the final germination rate was 54.5 % ±2.88). Additionally, germination was observed declining with increasing temperature in a saline environment. As germination is one of the major determinants of a species’ success, the possible spreading of this species under different climate change scenarios was simulated. Results of future distribution modelling suggested sensitivity to precipitation changes and potential northward expansion of this species. This research enhances our understanding of Iva xanthiifolia traits, useful for supporting management strategies for its control and mitigation of its impact, even under future climate change and land-use scenarios.

Jurassic to early Cretaceous geodynamic evolution of the eastern Hellenides

Remarkably well-preserved crustal sections from the Eastern Hellenides offer the chance to evaluate the regional geodynamic evolution of continental crust. Here, we identify and geodynamically interpret eclogite facies metamorphism in the Triassic–Jurassic volcano-sedimentary series of the West Circum Rhodope zone in the eastern Hellenides. Equilibrium phase diagrams and measured mineral compositions were used to inform reconstructions of geodynamic evolution during the early Alpine period. We newly define the existence of the Eohellenic HP belt, consisting of pre-Alpine basement, continental volcano-sedimentary formations, and a Jurassic magmatic arc. We suggest that the Eohellenic HP-Belt was subducted in the late Jurassic to early Cretaceous beneath the European plate margin. Detailed geodynamic reconstructions such as ours provide a robust anchor for future computational models of long-term crustal evolution in the Eastern Hellenides.Graphical abstract

A wide ranging experimental and kinetic modeling study of TMEDA pyrolysis and oxidation

Traditional liquid propellants like unsymmetrical dimethylhydrazine include several issues, such as toxicity, short storage time and preservation difficulty at low temperatures. For this reason, exploring green and stable propellant fuels is imperative in the development of propellants. Tetramethyl ethylenediamine (TMEDA) is considered to be a promising green propellant fuel with high specific impulse, non-toxicity, and long-term storage stability. Thus, investigating the combustion characteristics of TMEDA and constructing a detailed chemical kinetic mechanism are of fundamental importance in describing its combustion in computational fluid simulation. For this work, ignition delay times of TMEDA were measured in a shock tube at equivalent ratios of 0.5, 1.0 and 2.0, at pressures of 2, 10, and 20 bar, and in the temperature range of 870–1500 K with fixed fuel concentration at 2 %. After the reflected shock at 990–1500 K, the pyrolysis products of 2000 and 5000 ppm TMEDA in Argon were also measured at 5 and 10 bar conditions in a single pulse shock tube. In addition, laminar flame speeds of TMEDA were measured at initial pressures of 1 bar, initial temperatures of 333 and 353 K, and equivalence ratios ranging from 0.8 to 1.5 in a constant volume reactor. The experimental results were simulated using a detailed TMEDA kinetic mechanism. Reaction path analysis and sensitivity analysis were provided to clarify the chemical kinetics of TMEDA oxidation and pyrolysis. The current work provides new experimental data and fundamental analyses for understanding the oxidation and pyrolysis characteristics of TMEDA, and sheds light on optimal directions for future models.

The Ethics of Past and Future Memory Care Models: Perspectives from the Dutch and Danish Dementia Villages

AbstractDementia care organizations face a range of daunting environmental changes and challenges. Internationally, there is a long history of efforts to improve quality of care and life for individuals with dementia. In a time of particular attention to the images of older adults confined to a single or shared room in a care facility due to COVID‐19, autonomy becomes the overarching problem, not only because in general institutions limit the freedom of frail elders, but because the existential conditions that create the need for long term care such as chronic disease, cognitive decline, and the need for general support with activities of daily living (ADLs) rail against the autonomy of independent self‐sufficiency. Additionally, these environments are institutional in design and size, with little access to outdoor spaces and other amenities. This perspective manuscript addresses the ethics of past and future memory care models, looking specifically at the European Dementia Village model. This model allows for autonomy and continuation of patterns of daily living through housing integrated with large exterior walks and gardens, restaurants shops and amenities within familiar and normal surroundings, reducing resident anxiety and fear. This pioneering health care experiment negotiates rivaling discourses of intimacy, professionalization and medicalization. In order to get a deeper understanding of the culture and ethics of the model, the first two dementia village sites were visited which included meetings with care staff and administrators. Those conversations and observations led to a series of aligned themes relating to the ethics of the DV model which include: the strength of the social approach, clinical support, resident/staff collaborations and advocating for ethical dementia care.

An Optically Thin View of the Solar Chromosphere from Observations of the O i 1355 Å Spectral Line

The O i 1355 Å spectral line is one of the only optically thin lines that are both routinely observed and thought to be formed in the chromosphere. We present an analysis of a variety of observations of this line with the Interface Region Imaging Spectrograph (IRIS), and compare it with other IRIS diagnostics as well as diagnostics of the photospheric magnetic field. We utilize special deep exposure modes on IRIS and provide an overview of the statistical properties of this spectral line for several different regions on the Sun. We analyze the spatiotemporal variations of the line intensity and find that it is often significantly enhanced when and where magnetic flux of opposite polarities cancel. Significant emission occurs in association with chromospheric spicules. Because of the optically thin nature of the O i line, the nonthermal broadening can provide insight into unresolved small-scale motions. We find that the nonthermal broadening is modest, with typical values of 5–10 km s−1, and shows some center-to-limb variation, with a modest increase toward the limb. The dependence with the height of the intensity and line broadening off-limb is compatible with the line broadening being dominated by the superposition of Alfvén waves on different structures. The nonthermal broadening shows a modest but significant enhancement above locations that are in between photospheric magnetic flux concentrations in plage, i.e., where the magnetic field is likely to be more inclined with respect to the line of sight. Our measurements provide strict constraints on future theoretical models of the chromosphere.

High-Frequency Data Analysis for Volatility Prediction of the CSI 300 ETF: An Empirical Research Using HAR Family Models

In this paper, four HAR family models are used to study them and five-minute high-frequency trading data of the CSI 300 ETF is the target. Descriptive statistics is firstly conducted, and it is found that all variables have obvious autocorrelation and long-term memory which are suitable for time series analysis. Then, in-sample and out-of-sample analyses are implemented to test the predictive effect of each component on the volatility of the CSI 300 ETF and to compare the predictive ability of each model. The empirical results of the in-sample data show that daily and weekly realized volatility, continuous volatility, daily and monthly jump volatility, daily and monthly realized positive semi-variance, weekly and monthly realized negative semi-variance and positive and negative signed jump variation have stronger predictive effects on volatility of the CSI 300 ETF, while weekly realized volatility, jump volatility and realized positive semi-variance, and daily realized negative semi-variance have a weaker predictive effect on the CSI 300 ETF volatility. The results of the MCS test for out-of-sample prediction show that the HAR-RV-CJ model and HAR-RV-RSV model have significantly better out-of-sample predictive ability on the CSI 300 ETF volatility than the other two HAR family models, with HAR-RV-RSV model exhibiting the highest predictive accuracy in most cases. The above results indicate that the jump factor and the asymmetric factor of positive and negative returns can effectively improve the forecasting accuracy of HAR family models, so these two factors cannot be ignored in the construction of future HAR family models.

A Novel Method for Diagnosing Land–Atmosphere Coupling Sensitivity in a Single-Column Model

Abstract The response of boundary layer properties and cloudiness to changes in surface evaporative fraction (EF) is investigated in a single-column model to quantify the locally coupled impact of subgrid surface variations on the atmosphere during summer. Sensitive coupling days are defined when the model atmosphere exhibits large variations across a range of EFs centered on the analyzed value. Coupling sensitivity exists as both positive feedback (cloudiness increases with EF) and negative feedback (clouds increase with decreasing EF) regimes. The positive regime manifests in shallow convection situations, which are capped by a strengthened inversion and subsidence, restricting the vertical extent of convection to just above the boundary layer. Surfaces with larger EF (greater surface latent heat flux) can inject more moisture into the vertically confined system, lowering the cloud base and an increasing cloud liquid water path (LWP). Negative feedback regimes tend to manifest when large-scale deep convection, such as from mesoscale convective systems and fronts, is advected through the domain, where convection strengthens over surfaces with a lower EF (greater surface sensible heat flux). The invigoration of these systems by the land surface leads to an increase in LWP through strengthened updrafts and stronger coupling between the boundary layer and the free atmosphere. These results apply in the absence of heterogeneity-induced mesoscale circulations, providing a one-dimensional dynamical perspective on the effect of surface heterogeneity. This study provides a framework of intermediate complexity, lying between parcel theory and high-resolution coupled land–atmosphere modeling, and therefore isolates the relevant first-order processes in land–atmosphere interactions. Significance Statement Cloud formation, distribution, and other properties may be sensitive to heterogeneous surfaces depending on the strength and location of such heterogeneities and the background atmospheric state. This may drive differences in the cloud population depending on which part of the domain one is located. This may also lead to mesoscale circulations, which may strengthen or weaken this effect. Currently, climate models act on scales (∼100 km) that are too large to explicitly represent these processes, which are strongest at smaller scales (around 5–40 km). Therefore, subgrid-scale heterogeneity is neglected, and any predictability and model fidelity it may provide is lost. We use a simple model to diagnose sensitivity of the local atmosphere to surface variations meant to represent possible subgrid heterogeneity, providing a first-order estimate of its effect. We conclude that preferentially sensitive atmospheric states exist that lead to positive and/or negative feedback between land and atmosphere. This information is valuable to future climate model parameterizations aimed at improving the representation of these feedbacks.

Influence of Longitudinal and Lateral Forces on the Emission of Tire–Road Particulate Matter and Its Size Distribution

The objective of this study was to experimentally determine the mathematical correlations between the loading of the tire, being longitudinal and lateral forces, and the emission of particulate matter (PM) from the tire–road contact. Existing emission factors (EF, emission per vehicle and distance traveled) are the result of long-term measurements, which means that no conclusion can be drawn about the exact driving condition. To determine meaningful emission factors, extensive driving tests were conducted on an internal drum test bench while measuring PM emissions from the tire–road contact in real-time. This showed that the increases in emission over longitudinal and lateral forces can be approximated with fourth-order functions, with lateral forces leading to significantly higher emissions than longitudinal forces for the summer tire investigated. Using the emission functions obtained, a three-dimensional map was created that assigns an EF to each load condition consisting of different longitudinal and lateral forces for one vertical load. With known driving data, the map can be used for future simulation models to predict the total emission of real driving cycles. Furthermore, the results show that the average particle size increases with increasing horizontal force. The particles collected during the tests were analyzed to determine the proportions of tire and road material. According to the results, the tire contributes only about 20% of the particle mass, while 80% is attributable to the road surface. In terms of volume, these shares are 32% and 68%, respectively.

Generation and screening of mouse models of ADRD with multiple genetic manipulations to model dementia with mixed brain pathologies

AbstractBackgroundAlzheimer’s disease‐related dementias (ADRD) encompass a wide range of neurodegenerative diseases including Alzheimer’s disease, vascular dementia, frontotemporal dementia (FTD), Lewy body dementia (LBD), and mixed dementia [1‐4]. ADRDs are distinguished by the presence of pathologies including amyloid, tau, TDP‐43 inclusions, alpha‐synuclein aggregates, cerebral amyloid angiopathy, and/or hippocampal sclerosis [4‐9]. Consequently, the heterogeneity of ADRDs presents challenges for animal modeling of disease mechanisms. Our laboratory has demonstrated that genetic diversity in disease modeling is critical to recapitulate the molecular and cognitive features of AD [1,9, 10]. Here, we introduce multiple genetic manipulations to develop novel translational models of ADRD.MethodPrecision models of ADRD on multiple genetic backgrounds were generated via combinatorial CRISPR editing to knock‐in human disease‐causing mutations onto the C57BL/6J (B6) strain and crossed to either B6, DBA/2J (D2), FVB, or WSB to generate a set of 20 unique ADRD models (Fig.1). The effect of homozygous mutations were evaluated on the B6 background. Each model was assessed using a high‐capacity multi‐domain go/no‐go screen to prioritize candidate strains for future ADRD modeling of drug discovery, preclinical testing, and disease pathology.ResultAssays included frailty, open field, y‐maze, rotarod, and contextual fear conditioning. Models with one or more late‐onset behavioral deficits (12 mo) were a “go”; whereas, early behavioral deficits (6 and 9 mo), aggression, high attrition, and low fecundity and additional factors were considered unsuitable (“no‐go”). We identified several promising models of ADRD that exhibited late‐onset frailty and motor deficits as function of mutation burden and genetic background (e.g., App/Psen1/Tardbp/Mapt on the D2 background). The FVB background strain exhibited early frailty deficits (6 mo), and thus considered unsuitable (“no‐go”). The WSB background strain exhibited reserve across multiple frailty and behavioral assays up to 12 mo.ConclusionOur results demonstrate that combining ADRD mutations in genetically distinct backgrounds is a promising strategy to generate new mouse models that better model the human mid and late‐life onset of deficits associated with ADRD. The resulting model(s) will be translationally relevant and provide important insight into disease mechanisms for the future of ADRD research and drug discoveries.

Clarification of the Present Understanding of the Assessment of an Organization’s Digital Readiness in SMEs

The role of digital transformation (DT) in economic development is a vital and recurring point of research. It is particularly relevant if we consider the high percentage of digital transformation initiatives that fail to deliver the expected results, particularly in Small and Medium Enterprises (SMEs). This paper analyzes what is needed to make this transformation successful from an implementation perspective and, simultaneously, from the standpoint of obtaining the company’s expected results. This phenomenon is even more critical to decipher and understand when we look at the small and medium enterprises that face more significant challenges due to the scarcity of resources and needed skills. This work reviews a large variety of models through an extensive systematic literature review (SLR) that assess the readiness and maturity of the digital transformation of enterprises, with a focus on SMEs, with its primary objectives being (1) to review the existing studies and models that assess an organization’s maturity and readiness in the context of digital transformation, focusing on SMEs; (2) to identify if there are gaps considering the importance of the SMEs; and (3) to propose a standardized set of dimensions that should always be considered in a digital transformation assessment. The outcome of this research provides an essential contribution by identifying apparent gaps in the assessment of digital transformation in SMEs and proposing a scalable and standardized set of categories and subcategories that can be used across any future assessment model. These contributions are even more relevant when referencing minimal deep research in the context of SMEs and Digital Transformation. Doi: 10.28991/ESJ-2023-07-06-025 Full Text: PDF