Modeling Solution Research Articles

Transformative, Transdisciplinary, Transcendent Digital Education: Synergy, Sustainability and Calamity

Dynamic transformation of the knowledge economy, enhanced by Industry 4.0/5.0 development and rise of the networked society in the Digital Age, emergency digitization of all social communicative spheres due to pandemic measures have imposed dramatic changes onto transdisciplinary overlap in different areas of human knowledge and experience, induced by the cross-sectorial job market demands of university level education, curriculum design and learning outcomes. The global pandemic and subsequent warfare in Ukraine induced amplified digitalization measures in the higher education sphere. This end-to end digital shift in the educational processes (communication, content, outcomes and outputs, skills) heralded the introduction of meta-disciplinary dimensions of learning – digital, hybrid and, blended. These meta-disciplinary dimensions can be considered conduits of vertical (endocentric) and horizontal (exocentric) transdisciplinary of digital education as a sustainable dynamic system. Applied trans-disciplinary lens contributes to the solution of holistic modeling of processes and results of updating models and mechanisms of the highly dynamic communication system of education in the digital environment as a whole and its individual formats in the emergency digitization measures of different types.

Portable laser scanning solutions for 3D modelling of large buildings

Portable laser scanning solutions for 3D modelling of large buildings

Ray-Tracing modeling for urban photovoltaic energy planning and management

The traditional Radiative Transfer Modelling solutions for Solar Energy monitoring and forecasting often provide outputs for a single point location or an area location. However, for high resolution representation of areas these solutions suffer due to low simulation speeds. This approach makes it difficult for decision-makers to accurately estimate the solar potential of the administrative area and plan installations accordingly. In this direction, the study introduces three-dimensional Ray-Tracing based radiative modeling which is a high-speed area-based solution for solar energy monitoring. The three-dimensional ray-tracing was simulated by using advanced graphic creation platforms and cloud computing in conjunction with satellite data of the clouds, aerosols, building shadows effects and three-dimensional representations of the city using Cesium 3D tiles and Unreal Engine ®. The entire system was developed in a hybrid model to be exploited by urban planners for solar PV installations and by electricity distribution system operators for energy management and efficient incorporation of the produced energy into the regular and smart grids. This study implements and analyses this Ray-Tracing model for solar photovoltaic energy potential estimation at a rooftop level for the city of Athens, Greece. The total rooftop exploitable area in Athens was found to be close to 34 km2, which is able to massively host distributed PVs followed by almost 4.3 TWh of annually produced energy, whilst Penteli (a Municipality in Athens) possessed a potential of 96.8 GWh with an exploitable area of just 0.8 km2. This amount of energy, in a hypothetical full coverage scenario, is able to provide for 48.7% of Athens’s total energy requirement. Similar year-long simulations were conducted using the EU’s largest rooftop solar installation at Stavros Niarchos Foundation Cultural Center and randomly selected rooftops having solar installations in different municipalities in Athens. With these estimated solar potential values, the gross savings in natural gas consumption and hence the CO2 equivalent emissions can be computed. With the current estimated solar potential of Athens, the analyzed savings accounted of nearly 2.43 billion euros and 18 MT CO2 equivalent emissions. These computed annual savings are capable of covering installation costs for nearly 100,000 new solar installations. The end-product of this study is the development of a solar cadastre web tool which will support the decision-makers in the energy transition policies and the solar PV penetration into the urban environment and eventually drive the effort into renewable energy transition across the globe.

Maturing hiPSC-cardiomyocytes in cardiac microtissues for accurate preclinical in vitro modelling of long-QT syndrome type 1

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): reNEW - Novo Nordisk Foundation Center for Stem Cell Medicine Background Long-QT syndrome type 1 (LQT1), a cardiac arrhythmia often leading to sudden cardiac death, is due to mutations in KCNQ1 gene. Human iPSC-derived cardiomyocytes (hiPSC-CMs) have been widely used to model LQT1, promoting the concept of their use in preclinical studies. However, KCNQ1 locus is subjected to developmentally regulated genomic imprinting in the heart: in fetal cardiomyocytes the paternal allele is repressed, while both alleles become expressed after birth. Since hiPSC-CMs are typically immature fetal-like cells, residual imprinting may affect evaluation of KCNQ1 mutations in this system. Purpose This study wants to address the imprinting status of KCNQ1 in hiPSC-CMs and propose maturing hiPSC-CMs as a solution for reliable LQT1 modelling. Methods We derived hiPSC-CMs from a LQT1 patient carrying a heterozygous KCNQ1 mutation on the paternal allele and compared their electrical properties with the isogenic corrected hiPSC-CMs by patch clamp. We analysed KCNQ1 expression by ddPCR and DNA methylation. We used three-dimensional cardiac microtissues (MTs) composed by hiPSC-CMs, hiPSC-derived cardiac fibroblasts and endothelial cells to induce cardiomyocte maturation. Results LQT1 and isogenic corrected hiPSC-CMs showed no difference in action potential duration (APD). We hypothesized that residual KCNQ1 imprinting in hiPSC-CMs due to their immaturity was masking the phenotype. When we analyzed different hiPSC-CM lines, we found in all an unbalanced KCNQ1 allelic expression, with paternal allele accounting for 5-15% of the total. We then investigated whether maturing hiPSC-CMs could remove KCNQ1 imprinting. We included hiPSC-CMs in MTs, as this system previously showed to promote functional maturation and upregulation of KCNQ1. Here, we observed a substantial increased expression of the paternal allele and a reduction in the expression of KCNQ1OT1, the long non-coding RNA inducing paternal allele repression. LQT1 hiPSC-CMs dissociated from MTs showed prolonged APD compared to the corrected line. We also demonstrated that this was due to the reduction of the ionic current mediated by KCNQ1 (IKs) in LQT1 hiPSC-CMs compared to the corrected controls, thus revealing the mutation causative effect. Conclusions Our findings show that residual KCNQ1 imprinting in immature hiPSC-CMs can lead to underestimate (or overestimate) functional effects of pathological variants based on the parental allele that carries the mutation. This is overcome by maturation of hiPSC-CMs in tri-cellular cardiac microtissue which promotes KCNQ1 bi-allelic expression and allow dissecting mutation mechanisms. This study brings attention to the epigenetic regulation of hiPSC models and demonstrates the utility of using matured hiPSC-CMs as in vitro preclinical model for cardiac arrhythmias.

Highly Accurate and Efficient Deep Learning Paradigm for Full-Atom Protein Loop Modeling with KarmaLoop.

Protein loop modeling is a challenging yet highly nontrivial task in protein structure prediction. Despite recent progress, existing methods including knowledge-based, abinitio, hybrid, and deep learning (DL) methods fall substantially short of either atomic accuracy or computational efficiency. To overcome these limitations, we present KarmaLoop, a novel paradigm that distinguishes itself as the first DL method centered on full-atom (encompassing both backbone and side-chain heavy atoms) protein loop modeling. Our results demonstrate that KarmaLoop considerably outperforms conventional and DL-based methods of loop modeling in terms of both accuracy and efficiency, with the average RMSDs of 1.77 and 1.95 Å for the CASP13+14 and CASP15 benchmark datasets, respectively, and manifests at least 2 orders of magnitude speedup in general compared with other methods. Consequently, our comprehensive evaluations indicate that KarmaLoop provides a state-of-the-art DL solution for protein loop modeling, with the potential to hasten the advancement of protein engineering, antibody-antigen recognition, and drug design.

Analysis, Modeling, and Simulation Solution of Induced-Draft Fan Rotor with Excessive Vibration: A Case Study

In the modern industry, computer modeling and simulation tools have become fundamental to estimating the behavior of rotodynamic systems. These computational tools allow analyzing possible modifications as well as alternative solutions to changes in design, with the aim of improving performance. Nowadays, rotodynamic systems, present in various industrial applications, require greater efficiency and reliability. Although there are deep learning methodologies for monitoring and diagnosing failures which improve these standards, the main challenge is the lack of databases for learning, a problem that can be addressed through experimental monitoring and computer analysis. This work analyzes the vibrations of two induced-draft fans with excess vibration in a thermoelectric plant in Mexico. A vibration analysis was carried out through the instrumentation and monitoring of accelerometers located at crucial points in the fans. The results of this experimental analysis were validated by computer simulation based on FEM. The results show that the operating speed of the induced-draft fans is very close to their natural frequency, causing considerable stress and potential failures due to excessive vibration. Finally, this work presents a practical solution to modify the natural frequency of induced-draft fans, so that they can function correctly at the required operating speed, thus mitigating excessive vibration issues.

Analysis of Heat Transfer of Molten Salts Startup Flow in Cold Pipes Avoiding Freezing in Solar and Nuclear Energy Systems

Abstract Molten salts are employed as the heat transfer fluid to carry the thermal energy from a solar receiver or a nuclear reactor for delivering to thermal storage systems or thermal power plants for power generation. For the startup operation, molten salts need to be pumped to flow into the pipes which may have lower temperature than the freezing point of molten salt due to the cold ambient temperature overnight or over the suspension of operation. Preventing the freezing of molten salt in cold pipes becomes a critical issue to the safe operation of a concentrating solar thermal power plant or a molten salt nuclear power plant. This study conducted a basic heat transfer analysis of the transient heat transfer from flowing molten salt to a cold pipe to determine the length from entrance to the onset of freezing of the fluid. From our modeling and analytical solution using method of characteristics, the correlation of the location of onset of freezing of molten salt with respect to the flow velocity, heat capacities of molten salt and pipes, dimension of the pipes, and the initial temperatures of salts and pipes, have been understood clearly. The modeling and computational tool can fundamentally help engineers to design a system to avoid freezing and clogging at cold startup when molten salt is applied as a heat transfer fluid.

Coupled Experimental Study and Thermodynamic Modeling of the Fe–Si–N System

Some key experiments are carried out to determine the solubility of N in Fe–Si liquid solutions at various compositions, temperatures, and N2 pressures using the sampling method. The Fe–Si–N system is thermodynamically modeled using the CALculation of PHAse Diagrams (CALPHAD) approach for the first time based on critical evaluation of all available experimental data. In the present thermodynamic modeling, the liquid and solid solutions of the Fe–Si–N system are described using the modified quasichemical model and compound energy formalism, respectively. In particular, the Gibbs energies of liquid, FCC_A1, and BCC_A2 solutions are carefully determined to resolve the discrepancies among existing experimental data. The solubility product of β‐Si3N4(s) in Si steels and ferrosilicon alloys, activity coefficients, and interaction parameters of N in liquid and solid Fe–Si–N solutions are determined over a wide temperature range. As an application of the database, the N solubility in Si steels and phase constitution of nitrided ferrosilicon alloys in a wide composition, temperature, and N2 pressure ranges are predicted.

Pemberian Kompensasi Strategi dan Implikasi dalam Konteks Organisasi Modern

In the pursuit of business activities, companies require various resources such as capital, materials, machinery, and notably, human resources represented by employees. Employees, possessing talents, energy, and creativity, play a crucial role in achieving the company's objectives. This reciprocal relationship is driven by the employees' needs, motivating them to work. In this research, one shot time horizon is used with cross-section data type. Descriptive hypothesis testing using the technique of determining the average score. In verification research, the modeling approach and solution technique that will be used as an analysis tool is the variant or component-based structural equation modeling (SEM) method, namely PLS (partial least square). The results include that competitive salaries are an important component of an effective compensation strategy. Attractive perks and benefits can help organizations attract and retain the best employees. An objective and transparent performance appraisal system can help to ensure that high-performing employees receive appropriate compensation.

Optical redox imaging to screen synthetic hydrogels for stem cell-derived cardiomyocyte differentiation and maturation.

Heart disease is the leading cause of death in the United States, yet research is limited by the inability to culture primary cardiac cells. Cardiomyocytes (CMs) derived from human induced pluripotent stem cells (iPSCs) are a promising solution for drug screening and disease modeling. Induced pluripotent stem cell-derived CM (iPSC-CM) differentiation and maturation studies typically use heterogeneous substrates for growth and destructive verification methods. Reproducible, tunable substrates and touch-free monitoring are needed to identify ideal conditions to produce homogenous, functional CMs. We generated synthetic polyethylene glycol-based hydrogels for iPSC-CM differentiation and maturation. Peptide concentrations, combinations, and gel stiffness were tuned independently. Label-free optical redox imaging (ORI) was performed on a widefield microscope in a 96-well screen of gel formulations. We performed live-cell imaging throughout differentiation and early to late maturation to identify key metabolic shifts. Label-free ORI confirmed the expected metabolic shifts toward oxidative phosphorylation throughout the differentiation and maturation processes of iPSC-CMs on synthetic hydrogels. Furthermore, ORI distinguished high and low differentiation efficiency cell batches in the cardiac progenitor stage. We established a workflow for medium throughput screening of synthetic hydrogel conditions with the ability to perform repeated live-cell measurements and confirm expected metabolic shifts. These methods have implications for reproducible iPSC-CM generation in biomanufacturing.

Towards Improved Privacy in Digital Marketing: A Unified Approach to User Modeling with Deep Learning on a Data Monetization Platform

This paper introduces an innovative method for safeguarding user privacy in digital marketing campaigns through the application of deep learning techniques on a data monetization platform. This framework empowers users to maintain authority over their personal data while enabling marketers to pinpoint suitable target audiences. The system consists of several key stages Data representation learning in hyperbolic space captures latent user interests across various data sources with hierarchical structures. Subsequently, Generative Adversarial Networks generate synthetic user interests from these embedding. To preserve user privacy, Federated Learning is utilized for decentralized user monetization, Data privacy, modeling training, ensuring data remains undisclosed to marketers. Lastly, a hyperbolic embedding, Federated learning targeting strategy, rooted in recommendation systems, utilizes learned user interests to identify optimal target audiences for digital marketing campaigns. In sum, this approach offers a comprehensive solution for privacy-preserving user modeling in digital marketing.

Developing a simple and efficient modeling solution for predicting key phenological stages of table grapes in a non-traditional viticulture zone in south Asia.

Phenological shifts are one of the most visible signs of climatic variability and change in the biosphere. However, modeling plant phenological responses has always been a key challenge due to climatic variability and plant adaptation. Grapevine is a phenologically sensitive crop and, thus, its developmental stages are affected by the increase in temperature. The goal of this study was to develop a temperature-based grapevine phenology model (GPM) for predicting key developmental stages for different table grape cultivars for a non-traditional viticulture zone in south Asia. Experiments were conducted in two vineyards at two locations (Chakwal and Islamabad) in the Pothawar region of Pakistan during the 2019 and 2020 growing seasons for four cultivars including Perlette, King's Ruby, Sugraone and NARC Black. Detailed phenological observations were obtained starting in January until harvest of the grapes. The Mitscherlich monomolecular equation was used to develop the phenology model for table grapes. There was a strong non-linear correlation between the Eichhorn and Lorenz phenological (ELP) scale and growing degree days (GDD) for all cultivars with coefficient of determinations (R2) ranging from 0.90 to 0.94. The results for model development indicated that GPM was able to predict phenological stages with high skill scores, i.e., a root mean square (RMSE) of 2.14 to 2.78 and mean absolute error (MAE) of 1.86 to 2.26 days. The prediction variability of the model for the onset timings of phenological stages was up to 3 days. The results also reveal that the phenology model based on GDD approach provides an efficient planning tool for viticulture industry in different grape growing regions. The proposed methodology, being a simpler one, can be easily applied to other regions and cultivars as a predictor for grapevine phenology.

On thermal and strain-rate dependences of polymethacrylimide (PMI) foam materials

Polymethacrylimide (PMI) foams exhibit great potentials in engineering applications thanks to its outstanding lightweight and superior mechanical properties. However, the temperature and loading rate can greatly affect its mechanical behaviors, which are the key factors in analysis and design of the PMI foam structures for practical applications. Unfortunately, there is lack of fundamental data and constitutive models available concerning the elastic-plastic properties of the PMI foam in various stress states at different ambient temperatures and different strain rates. Therefore, this study aims to fill this knowledge gap by generating basic experimental data and providing a generic modeling solution for PMI foam materials and structures. To better reflect the actual service conditions in practice, different stress states are considered under different temperatures and strain rates. The cyclic loads are applied to characterize the actual mechanical responses of the PMI foam where the initial and subsequent yield surfaces were calibrated and analyzed. Following the experimental results, the elasticity, plasticity, viscosity and damage of the PMI foam are investigated thoroughly, especially for the initial and subsequent yielding behaviors. Further, the thermal and strain rate dependent constitutive models are established and calibrated based on the in-house experimental data for finite element (FE) modeling. A novel damage model is proposed to replicate the phenomenon of the stiffness degradation in compression. It is found that accurate predictions on the mechanical responses of PMI foam can be achieved under different temperatures and different strain rates. This study is anticipated to provide fundamental experimental data and effective constitutive models of PMI foams for real engineering applications, such as lightweight design of sandwich structures under some harsh conditions.

Hierarchical SVM for Semantic Segmentation of 3D Point Clouds for Infrastructure Scenes

The incorporation of building information modeling (BIM) has brought about significant advancements in civil engineering, enhancing efficiency and sustainability across project life cycles. The utilization of advanced 3D point cloud technologies such as laser scanning extends the application of BIM, particularly in operations and maintenance, prompting the exploration of automated solutions for labor-intensive point cloud modeling. This paper presents a demonstration of supervised machine learning—specifically, a support vector machine—for the analysis and segmentation of 3D point clouds, which is a pivotal step in 3D modeling. The point cloud semantic segmentation workflow is extensively reviewed to encompass critical elements such as neighborhood selection, feature extraction, and feature selection, leading to the development of an optimized methodology for this process. Diverse strategies are implemented at each phase to enhance the overall workflow and ensure resilient results. The methodology is then evaluated using diverse datasets from infrastructure scenes of bridges and compared with state-of-the-art deep learning models. The findings highlight the effectiveness of supervised machine learning techniques at accurately segmenting 3D point clouds, outperforming deep learning models such as PointNet and PointNet++ with smaller training datasets. Through the implementation of advanced segmentation techniques, there is a partial reduction in the time required for 3D modeling of point clouds, thereby further enhancing the efficiency and effectiveness of the BIM process.

Spatial-temporal modeling of oil condition monitoring: A review

Lubricating oil plays a vital role as the information carrier for equipment tribological performance. Therefore, oil condition monitoring (OCM) serves as a crucial technology for assessing and predicting state degradation, providing the first-line defense against functional failure. However, limited by random fluctuation and insufficient knowledge, the modeling of OCM has suffered from uncertainty problems, leading to poor applicability and insufficient generalizability. The existing reviews are less elaborated on the information uncertainty for description, characterization, and treatment, which expresses the essence of modeling constraints. To bridge this gap, the paper provides a comprehensive review of the oil state modeling in terms of uncertainty. The existing methods and metrics are reviewed from the perspective of the spatial oil state assessment (OCA) and temporal remaining useful life (RUL) prediction modeling. Further, the existing methods are analyzed to solve the uncertainty problem, and then the solutions in oil state modeling considering uncertainty are discussed. Finally, targeting the challenges of the monitoring technology, the future trends of OCM are presented.

LR-GLASSO Method for Solving Multiple Explanatory Variables of the Village Development Index

Sustainable Development Goals (SDGs) are developments that maintain sustainable improvement in society’s economic, social, and environmental welfare. Kemendes PDTT RI has issued the Village Development Index (VDI) to provide information and the status of village progress to support village development to improve the National SDGS. Modelling with multiple explanatory variables causes a high correlation between explanatory variables, multicollinearity, and coefficient estimation results, which have major variance and overfitting in prediction results. The modeling solution uses LASSO and GLASSO. Binary categorical response data uses binary logistic regression (LR), so LR-LASSO and LR-GLASSO are used. North Maluku Province has a VDI ranking that tends to fall in 2018-2022. Based on the mean and variance of the coefficient estimation results and misclassification errors, LR-GLASSO is better than LR-LASSO and LR. LR-GLASSO is recommended for analyzing VDI data because it has many explanatory variables, and the correlation between them is relatively high. The recommendation given by the Indonesian government, if it is to increase the status of VDI in Indonesia, especially in North Maluku province, is to increase the number of electricity users, food and beverage stalls, and other cooperatives. The Indonesian government also needs to pay attention to villages relatively far from the regent's office, between food and beverage stalls, and supporting health centres, because they still need to be developed compared to other villages, and more than 50% of villages are underdeveloped. If the Village SDGs are formulated through increasing VDI status, it will support the achievement of SDGs goals nationally.

PAGN: the one-stop solution for AGN disc modelling

ABSTRACT Models of accretion discs surrounding active galactic nuclei (AGNs) find vast applications in high-energy astrophysics. The broad strategy is to parametrize some of the key disc properties such as gas density and temperature as a function of the radial coordinate from a given set of assumptions on the underlying physics. Two of the most popular approaches in this context were presented by Sirko & Goodman and Thompson et al. We present a critical reanalysis of these widely used models, detailing their assumptions and clarifying some steps in their derivation that were previously left unsaid. Our findings are implemented in the pAGN module for the Python programming language, which is the first public implementation of these accretion-disc models. We further apply pAGN to the evolution of stellar-mass black holes embedded in AGN discs, addressing the potential occurrence of migration traps.

The fractional analysis of thermo-elasticity coupled systems with non-linear and singular nature

It is mentioned that understanding linear and non-linear thermo-elasticity systems is important for understanding temperature, elasticity, stresses, and thermal conductivity. One of the most crucial aspects of the current research is the solution to these systems. The fractional form of several thermo-elastic systems is explored, and elegant solutions are provided. The solutions of fractional and integer thermo-elastic systems are further discussed using tables and diagrams. The closed contact between the LRPSM and exact solutions is displayed in the graphs. Plotting fractional problem solutions demonstrates their convergence towards integer-order problem solutions for suitable modeling. The tables confirm that greater precision is rapidly attained as the terms of the derived series solution increase. The faster convergence and stability of the suggested method support its modification for other fractional non-linear complex systems in nature.

Numerical and statistical analyses of three-dimensional non-axisymmetric Homann's stagnation-point flow of nanofluids over a shrinking surface

Nanofluids, which are known for their superior heat transfer properties, have become indispensable in real-world applications. This underscores the critical importance of comprehensive understanding, which forms the core of this study. The goal of this study is to scrutinize Homann's non-axisymmetric stagnation-point flow over a shrinking surface of various single-phase water-based nanofluids (water-copper, water-alumina, and water-titanium dioxide) in a three-dimensional (3D) system. The model formulations are derived into ordinary differential equations via sophisticated similarity variables that satisfy the continuity equation. A boundary value problem solver (bvp4c) with a finite difference method in MATLAB is adapted to carry out numerical calculations while the response surface methodology (RSM) in MINITAB is used to statistically analyze the solution. The opposing flow created by the shrinking surface within a certain degree of shrinkage leads to the formation of two distinct alternative solutions. The current study offers two alternative solutions for boundary layer flow control modeling. However, only the first solution is tested to be stable. The critical point for boundary layer bifurcation is identified, to efficiently manage the flow. Water-copper nanofluid has been proven to perform better in heat transfer than water-alumina and water-titanium dioxide nanofluids. The 2 % volume fraction of copper provides better heat transfer compared to 1 % volume fraction in the present configuration of the flow system. The response optimizer via RSM also validates that the heat transfer performance of copper nanofluid is maximized when the volume fraction of copper is set at a high level with a low shear-to-strain rate.

Bayesian modelling disentangles language versus executive control disruption in stroke.

Stroke is the leading cause of long-term disability worldwide. Incurred brain damage can disrupt cognition, often with persisting deficits in language and executive capacities. Yet, despite their clinical relevance, the commonalities and differences between language versus executive control impairments remain under-specified. To fill this gap, we tailored a Bayesian hierarchical modelling solution in a largest-of-its-kind cohort (1080 patients with stroke) to deconvolve language and executive control with respect to the stroke topology. Cognitive function was assessed with a rich neuropsychological test battery including global cognitive function (tested with the Mini-Mental State Exam), language (assessed with a picture naming task), executive speech function (tested with verbal fluency tasks), executive control functions (Trail Making Test and Digit Symbol Coding Task), visuospatial functioning (Rey Complex Figure), as well as verbal learning and memory function (Soul Verbal Learning). Bayesian modelling predicted interindividual differences in eight cognitive outcome scores three months after stroke based on specific tissue lesion topologies. A multivariate factor analysis extracted four distinct cognitive factors that distinguish left- and right-hemispheric contributions to ischaemic tissue lesions. These factors were labelled according to the neuropsychological tests that had the strongest factor loadings: One factor delineated language and general cognitive performance and was mainly associated with damage to left-hemispheric brain regions in the frontal and temporal cortex. A factor for executive control summarized mental flexibility, task switching and visual-constructional abilities. This factor was strongly related to right-hemispheric brain damage of posterior regions in the occipital cortex. The interplay of language and executive control was reflected in two distinct factors that were labelled as executive speech functions and verbal memory. Impairments on both factors were mainly linked to left-hemispheric lesions. These findings shed light onto the causal implications of hemispheric specialization for cognition; and make steps towards subgroup-specific treatment protocols after stroke.