Modeling Approach Research Articles

Rationalization of a Streptavidin Based Enantioselective Artificial Suzukiase: An Integrative Computational Approach.

An Artificial Metalloenzyme (ArM) built employing the streptavidin-biotin technology has been used for the enantioselective synthesis of binaphthyls by means of asymmetric Suzuki-Miyaura cross-coupling reactions. Despite its success, it remains a challenge to understand how the length of the biotin cofactors or the introduction of mutations to streptavidin leads the preferential synthesis of one atropisomer over the other. In this study, we apply an integrated computational modeling approach, including DFT calculations, protein-ligand dockings and molecular dynamics to rationalize the impact of mutations and length of the biotion cofactor on the enantioselectivities of the biaryl product. The results unravel that the enantiomeric differences found experimentally can be rationalized by the disposition of the first intermediate, coming from the oxidative addition step, and the entrance of the second substrate. The work also showcases the difficulties facing to control the enantioselection when engineering ArM to catalyze enantioselective Suzuki-Miyaura couplings and how the combination of DFT calculations, molecular dockings and MD simulations can be used to rationalize artificial metalloenzymes.

European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions

Abstract. Soil bulk density (BD) serves as a fundamental indicator of soil health and quality, exerting a significant influence on critical factors such as plant growth, nutrient availability, and water retention. Due to its limited availability in soil databases, the application of pedotransfer functions (PTFs) has emerged as a potent tool for predicting BD using other easily measurable soil properties, while the impact of these PTFs' performance on soil organic carbon (SOC) stock calculation has been rarely explored. In this study, we proposed an innovative local modeling approach for predicting BD of fine earth (BDfine) across Europe using the recently released BDfine data from the LUCAS Soil (Land Use and Coverage Area Frame Survey Soil) 2018 (0–20 cm) and relevant predictors. Our approach involved a combination of neighbor sample search, forward recursive feature selection (FRFS), and random forest (RF) models (local-RFFRFS). The results showed that local-RFFRFS had a good performance in predicting BDfine (R2 of 0.58, root mean square error (RMSE) of 0.19 g cm−3, relative error (RE) of 16.27 %), surpassing the earlier-published PTFs (R2 of 0.40–0.45, RMSE of 0.22 g cm−3, RE of 19.11 %–21.18 %) and global PTFs using RF models with and without FRFS (R2 of 0.56–0.57, RMSE of 0.19 g cm−3, RE of 16.47 %–16.74 %). Interestingly, we found that the best earlier-published PTF (R2 = 0.84, RMSE = 1.39 kg m−2, RE of 17.57 %) performed close to the local-RFFRFS (R2 = 0.85, RMSE = 1.32 kg m−2, RE of 15.01 %) in SOC stock calculation using BDfine predictions. However, the local-RFFRFS still performed better (ΔR2 > 0.2) for soil samples with low SOC stocks (< 3 kg m−2). Therefore, we suggest that the local-RFFRFS is a promising method for BDfine prediction, while earlier-published PTFs would be more efficient when BDfine is subsequently utilized for calculating SOC stock. Finally, we produced two topsoil BDfine and SOC stock datasets (18 945 and 15 389 soil samples) at 0–20 cm for LUCAS Soil 2018 using the best earlier-published PTF and local-RFFRFS, respectively. This dataset is archived on the Zenodo platform at https://doi.org/10.5281/zenodo.10211884 (S. Chen et al., 2023). The outcomes of this study present a meaningful advancement in enhancing the predictive accuracy of BDfine, and the resultant BDfine and SOC stock datasets for topsoil across the Europe enable more precise soil hydrological and biological modeling.

Testing the waters! The role of ethical leadership towards innovative work behavior through psychosocial well-being and perceived organizational support

PurposeIn response to the dynamic demands of the contemporary business landscape, this study critically examines the pivotal role of ethical leadership in shaping employee’s innovative behavior within organizations. Our research delves into the nuanced interplay between ethical leadership, psychological well-being and innovative work behavior. Drawing from the principles of social exchange theory, our study addresses a critical gap in the literature by exploring the mediating role of psychological well-being in the relationship between ethical leadership and employees' innovative work behavior.Design/methodology/approachIn this quantitative research, data were collected from 384 employees and their direct supervisors in Pakistan’s IT sector using “Google Forms” through a convenience sampling method facilitated by the “LinkedIn” platform. Additionally, the study applied a two-stage structural equation modeling approach, first to assess the uni-dimensionality, and subsequently, to evaluate the proposed hypotheses.FindingsThe research results unveiled a robust and positive impact of ethical leadership on innovative work behavior, operating through both direct and indirect pathways mediated by psychological well-being. Intriguingly, the moderating role of perceived organizational support adds depth to our understanding, revealing nuanced conditions under which ethical leadership influences employees' well-being and, subsequently, their innovative contributions.Practical implicationsBeyond theoretical contributions, our study provides practical insights for managers seeking to leverage employees' innovative work behavior for organizational success. By emphasizing ethical leadership as a catalyst, we advocate for its integration into HRM practices. However, recognizing the contextual nature of organizational support, our findings underscore the importance of adaptable leadership strategies to maximize positive outcomes.Originality/valueGrounded in the principles of social exchange theory, this research marks a pioneering effort to shed light on the link between ethical leadership and innovative work behavior through the mediation of psychological well-being. Additionally, this study makes a valuable contribution to the current body of knowledge by investigating the contingent influence of perceived organizational support on the relationship between ethical leadership and employees' psychological well-being.

Addressing uncertainty in Participatory Integrated Assessment: qualitative modeling approach for risk estimation

Participatory Integrated Assessment (PIA) has become a vital tool for decision-making for sustainable development, but it faces significant challenges due to the inherent uncertainty of socio-ecological systems. Uncertainty arises from multiple sources, such as incomplete data, knowledge gaps, and unpredictable events, which can lead to inadequate risk estimations and potentially undermine the effectiveness of environmental planning efforts. To address these challenges, this study proposes a qualitative modeling approach for risk estimation in PIA. The approach employs Decision Making under Deep Uncertainty (DMDU) to combine qualitative insights and information from stakeholders with available quantitative data. It allows for the exploration of alternative future states of the world and the identification of robust scenarios that promote sustainable development. The effectiveness of the proposed approach is demonstrated through the Ecological Ordinance of Yucatán, Mexico, a policy-making tool for multi-sectoral environmental planning. The study shows how qualitative DMDU can identify critical uncertainties and provide insights into regional management strategies. It also emphasizes the importance of stakeholder engagement and transparency in the decision-making process. Overall, this study presents a promising approach for addressing multiple forms of uncertainty in PIA and improving ecological risk estimation for decision-making in complex socio-ecological systems.

Economic impacts of climate change on forests: a PICUS-LANDIS-CGE modeling approach

Climate change is expected to alter both forest stand- and landscape-level dynamics through a change in environmental characterises. While numerous studies have employed models to assess the ecological and/or economic impacts of such changes on forests throughout the world, there is need to further refine such analyses. In this paper, we contribute to this literature by coupling an ecological (PICUS-LANDIS II) modeling framework with an economic (CGE) model to better account for the economic impact associated with climate-induced impacts on forest stand and landscape-level structure and composition dynamics. Applying this framework to a case-study region of New Brunswick, Canada, we estimate that climate change will reduce softwood supply by 16-73% and impact hardwood supply in the range of -2% to +4% by 2150. The change in wood supply is estimated to reduce the value of the softwood and hardwood forestry and logging sector output by up to 51% and 17%, respectively, by 2150. These sector-level impacts may lead to a 0.08-0.88% reduction in annual GDP by 2150. The methodological advances established in this study can be used to better inform future forest management and economic plans that aim to lessen both the ecological and economic impact of climate change.

Gaussian process regression for three-dimensional soil mapping over multiple spatial supports

This study investigates the complexity of spatial soil modelling, particularly focusing on the challenge of variable vertical support in traditional soil data collection. Traditional soil sampling, described in terms of horizons, often fails to accurately pinpoint the specific depths for specific soil properties. This gap is significant, as depth-specific data is crucial for a thorough understanding of soil formation processes and for assessing potential environmental impacts. In digital soil mapping (DSM), the prevalent reliance on standardised depth intervals and mass-preserving spline functions for data resampling results in a modelling approach that tends to disregard depth-related details, thereby introducing potential uncertainties.To address these limitations, this work explores how effectively Gaussian process regression (GPR) can model soil in 3D. This technique comprises two key components: a mean function and a semivariogram-like kernel. Unlike conventional methods that make a single prediction, GPR provides detailed probability distributions. This capability allows for the quantification of prediction uncertainty at various points, offering insights for decision-making and risk assessment purposes. Moreover, GPR has the capability to make volume or block estimates and assess associated uncertainties. Enabling volume-based predictions enriches the range of strategies available for land management.In this research, we employ GPR as a novel 3D soil modelling technique and compare its performance with traditional spline-based methods. The comparison is conducted through a case study on a farm in northern New South Wales, Australia, focusing on the 3D mapping of soil pH and electrical conductivity (EC). Our results demonstrate the ability of GPR to estimate soil properties across various volumes, utilising data from multiple vertical supports, thereby offering a more versatile approach for soil modelling in diverse spatial contexts.

The interplay effects of digital technologies, green integration, and green innovation on food supply chain sustainable performance: An organizational information processing theory perspective

Green integration (GIT) and green innovation (GIO) in food supply chains (FSCs) have the potential to attain sustainability with the facilitation of digital technologies (DTs). Nevertheless, few studies have been conducted regarding the effectiveness of DTs and GIT in determining GIO and sustained FSC. Furthermore, an unsustainable food supply chain is a globally recognised problem that needs immediate, multidimensional holistic measures. Therefore, the present study tests the interplay effects of GIT, GIO, and DTs in FSC sustainable performance (FSCSP) under the strategic configuration for information-processing fit, i.e., organizational information processing theory (OIPT) perspective. A combined approach of structural equation modelling (SEM) and artificial neural network (ANN) was used to examine the collected responses from different related industries and validate the robustness of the proposed hypothesis through ANN. The findings demonstrate that the usage of DTs has a favourable impact on GIO and GIT. Furthermore, GI has a positive influence on GIO. Finally, both GIO and GI have a positive impact on FSCSP. Theoretically, under the strategic alignment for information-processing fit, the study's findings suggest that GIT and GIO improve their FSC strategies and practically keep the FSC ahead of the competition for the long haul by ensuring sustainability. It is clear from the results of this research that practitioners should support more DTs to promote GIT, GIO, and FSCSP. This research fills a significant gap in the literature by analyzing the unexplored connections across the FSCSP, GIT, GIO, and DTs as facilitators in the context of sustainability.

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560–680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer.

Assessing sand sedimentation in drilling slurry using particle methods for ground excavation management

In the construction of cast-in-place concrete piles, residual sand portions in the drilling slurry that are not completely removed can significantly influence the strength and characteristics of the concrete piles. Understanding the sedimentation behavior of these sand portions during concrete placement is crucial to ensure pile quality. However, directly observing this process in real-time at construction sites is challenging, making it difficult to accurately assess the impact of the sand portions. When predicting the sedimentation of sand particles in drilling slurry, the conventionally used Stokes' equation can only express one-dimensional motion of a single particle size, but to accurately capture the real phenomenon, three-dimensional behavior must be considered. In this study, a numerical simulation method based on the particle method (MPS method) was used to visually evaluate the sedimentation process of sand in the drilling slurry. By modeling the drilling slurry and sand particles using the MPS method, the complex three-dimensional settling behavior of sand particles in the drilling slurry at both laboratory and full scales was successfully represented, demonstrating the effectiveness of this modeling approach. The MPS method allows for the analytical visualization of the three-dimensional sedimentation behavior, enabling real-time observation and quantitative evaluation of the sand sedimentation process. Using this method, the temporal evolution of the settling process of sand components in the drilling slurry can be tracked, providing valuable insights into factors that influence the quality of concrete piles. The results of this study demonstrate the potential of the MPS method for visually evaluating the settling behavior of sand components in drilling slurry. This approach offers a more accurate and reliable method for assessing the impact of residual sand on the strength and characteristics of cast-in-place concrete piles. By enhancing the understanding of this process, more effective foundation techniques can be developed to improve the safety and stability of structures built on soft ground, particularly in earthquake-prone regions where fine particles such as clay and silt pose additional challenges to construction practices.

Cutting force and surface roughness evaluation of cryogenic treated inserts for machining en24 grade alloy steel: RSM modelling approach

Cutting force and surface roughness evaluation of cryogenic treated inserts for machining en24 grade alloy steel: RSM modelling approach

Monte Carlo Simulations for Global and Local Interaction Buckling of Welded Box-sections

The present study focuses on investigating the interaction behavior of local and global buckling resistance of welded box-section columns by using stochastic analysis. Previous studies mainly investigated this failure mode using experimental or numerical investigations but using deterministic approaches. The current research employs Monte Carlo simulations as a robust numerical tool to estimate the interaction buckling resistance. Stochastic variables are defined, encompassing variabilities in geometrical and material properties, as well as local and global imperfections. To facilitate these simulations, a rigorously validated numerical model is employed, utilizing geometrical and materially nonlinear analysis with imperfections (GMNIA). This advanced modeling approach accounts for the complex nonlinearities inherent in the behavior of welded box-section columns. The characteristic resistance derived from the Monte Carlo simulation is compared to 1. test results, 2. analytical buckling resistance according to the Eurocode design approach, and 3. previously proposed deterministic approaches. Within the research program, an improved generalized design formula is developed to estimate the buckling resistance for pure local, global, and interaction buckling modes. The new design equation is compatible with the design rules of the Eurocode and enhances the ease of use of the standard regulations.

The effect of the COVID-19 health disruptions on breast cancer mortality for older women: a semi-Markov modelling approach

ABSTRACT Public health measures necessitated by the COVID-19 pandemic have affected cancer pathways by halting screening, delaying diagnostic tests and reducing the numbers starting treatment. Specifically, this moves individuals from observed and treated pathways to unobserved and untreated pathways. We introduce a semi-Markov model that includes both, extending an industry-based multiple state model used for life and critical illness insurance. Our model includes events related to cancer diagnosis and progression based on publicly available population data for women aged 65–89 in England and on relevant medical literature. We quantify age-specific excess deaths, for a period up to 5 years, along with years of life expectancy lost and changes in cancer mortality by cancer stage. Our analysis suggests a 3–6% increase in breast cancer deaths, and a 4–6% increase in registrations of advanced breast cancer, robust under sensitivity analysis. This should be applicable to actuarial work in areas where longevity and advanced age morbidity affect healthcare, retirement and insurance.

A fast scene geometric modeling approach for digital twins combining neural rendering and model retrieval

ABSTRACT Digital twin technology is a crucial driving technology for the realization of Industry 4.0, which enables simulation, analysis, and prediction by constructing geometric scenes that correspond to physical scenes. Current automation in scene modeling is limited, relying heavily on costly and inefficient manual operations. This hinders digital twin technology progress. This paper proposes a framework for a fast scene geometric modeling method that combines neural rendering and model retrieval for digital twins. Neural rendering techniques first train the collected image data, and the point cloud data of physical entities are rendered. Then based on the semantic mapping algorithm between the point cloud data of physical entities and the multi-view image data of 3D CAD models proposed in this study, the corresponding geometric models are retrieved from the geometric asset library by inputting the point cloud data, and all the retrieved geometric models are embedded in the geometric scene to complete the scene geometric modeling. Finally, in a case study of the digital twin-based scene construction for decommissioned lithium battery dismantling, the effectiveness of this method is demonstrated, which can improve the speed and reduce the cost of scene geometric modeling.

I Won't Forget to Do It If It's Important: A Multinomial Processing Tree Analysis of Social Importance and Monetary Reward on Event-Based Prospective Memory.

While previous research has suggested that prospective memory may be enhanced by providing a social motive (i.e., social importance) or by promising a monetary reward for successful performance, to the best of our knowledge, the underlying mechanisms responsible for these effects are still largely unexplored. In a sample of 96 younger adults, this study investigated how social importance and promising a monetary reward influence the prospective component and the retrospective component of event-based prospective memory separately, with the application of a multinomial modeling approach. Results revealed enhanced prospective memory performance for all importance conditions compared to a standard condition. This improvement was characterized by an increased allocation of resource-demanding attentional processes in performing the prospective memory task at the expense of the ongoing task without an increase in the perceived importance of the prospective memory task. The model-based analyses showed that the beneficial effects of importance arise from an increased engagement of the prospective component, leaving the estimates for the retrospective component unaffected.

Between Technological Utopia and Dystopia: Online Expression of Compulsory Use of Surveillance Technology

This study investigated people’s ethical concerns of surveillance technology. By adopting the spectrum of technological utopian and dystopian narratives, how people perceive a society constructed through the compulsory use of surveillance technology was explored. This study empirically examined the anonymous online expression of attitudes toward the society-wide, compulsory adoption of a contact tracing app that affected almost every aspect of all people’s everyday lives at a societal level. By applying the structural topic modeling approach to analyze comments on four Hong Kong anonymous discussion forums, topics concerning the technological utopian, dystopian, and pragmatic views on the surveillance app were discovered. The findings showed that people with a technological utopian view on this app believed that the implementation of compulsory app use can facilitate social good and maintain social order. In contrast, individuals who had a technological dystopian view expressed privacy concerns and distrust of this surveillance technology. Techno-pragmatists took a balanced approach and evaluated its implementation practically.

A window into the forest: post‐release behaviour of rehabilitated Bornean sun bears Helarctos malayanus euryspilus in Sabah, Malaysia

As the number of threatened wildlife species increases globally, captive rehabilitation and release of wild caught animals plays an increasingly significant role in conservation efforts, particularly where poaching threatens wild populations. Sun bear Helarctos malayanus populations are decreasing globally, with habitat loss and fragmentation and poaching as key threats to the species. This study aimed to assess post‐release behaviour of rehabilitated sun bears (n = 12) using GPS‐collar data to inform ongoing efforts to rehabilitate wild caught bears. We assessed spatial patterns of movement using kernel density estimates and utilisation distributions. We utilised an information theory (IT) and generalised linear modelling approach to assess temporal patterns of sun bear movement (n = 9). Post‐release home range estimates (mean = 56.68 km2, range:1.9–202.4) and behaviour varied widely between bears, with no significant difference in home range size between males (n = 3) and females (n = 6). Home ranges of most bears stabilised within the first month. Bears that were rehabilitated and released together did not spend a significant amount of time together following release. Bears were most active during daytime hours and most established a post‐release home range encompassing the riverside. The ultimate success of the releases is unknown, due to collar malfunction and damage, and the inaccessibility of the study terrain. We find that future releases of sun bears would benefit from soft‐release and in situ rehabilitation techniques. This study provides important insights into the complexity of bear reintroductions informing future rehabilitation and releases for threatened bear species.

Opioid Coprescription Through Risk Mitigation Guidance and Opioid Agonist Treatment Receipt

At the onset of the COVID-19 pandemic, the government of British Columbia, Canada, released clinical guidance to support physicians and nurse practitioners in prescribing pharmaceutical alternatives to the toxic drug supply. These alternatives included opioids and other medications under the risk mitigation guidance (RMG), a limited form of prescribed safer supply, designed to reduce the risk of SARS-CoV-2 infection and harms associated with illicit drug use. Many clinicians chose to coprescribe opioid medications under RMG alongside opioid agonist treatment (OAT). To examine whether prescription of hydromorphone tablets or sustained-release oral morphine (opioid RMG) and OAT coprescription compared with OAT alone is associated with subsequent OAT receipt. This population-based, retrospective cohort study was conducted from March 27, 2020, to August 31, 2021, included individuals from 10 linked health administrative databases from British Columbia, Canada. Individuals who were receiving OAT at opioid RMG initiation and individuals who were receiving OAT and eligible but unexposed to opioid RMG were propensity score matched at opioid RMG initiation on sociodemographic and clinical variables. Data were analyzed between January 2023 and February 2024. Opioid RMG receipt (≥4 days, 1-3 days, or 0 days of opioid RMG dispensed) in a given week. The main outcome was OAT receipt, defined as at least 1 dispensed dose of OAT in the subsequent week. A marginal structural modeling approach was used to control for potential time-varying confounding. A total of 4636 individuals (2955 [64%] male; median age, 38 [31-47] years after matching) were receiving OAT at the time of first opioid RMG dispensation (2281 receiving ongoing OAT and 2352 initiating RMG and OAT concurrently). Opioid RMG receipt of 1 to 3 days in a given week increased the probability of OAT receipt by 27% in the subsequent week (adjusted risk ratio, 1.27; 95% CI, 1.25-1.30), whereas receipt of opioid RMG for 4 days or more resulted in a 46% increase in the probability of OAT receipt in the subsequent week (adjusted risk ratio, 1.46; 95% CI, 1.43-1.49) compared with those not receiving opioid RMG. The biological gradient was robust to different exposure classifications, and the association was stronger among those initiating opioid RMG and OAT concurrently. This cohort study, which acknowledged the intermittent use of both medications, demonstrated that individuals who were coprescribed opioid RMG had higher adjusted probability of continued OAT receipt or reengagement compared with those not receiving opioid RMG.

Biophysics of frequency-dependent variation in paresthesia and pain relief during spinal cord stimulation.

The neurophysiological effects of spinal cord stimulation (SCS) for chronic pain are poorly understood, resulting in inefficient failure-prone programming protocols and inadequate pain relief. Nonetheless, novel stimulation patterns are regularly introduced and adopted clinically. Traditionally, paresthetic sensation is considered necessary for pain relief, although novel paradigms provide analgesia without paresthesia. However, like pain relief, the neurophysiological underpinnings of SCS-induced paresthesia are unknown. Here, we paired biophysical modeling with clinical paresthesia thresholds (of both sexes) to investigate how stimulation frequency affects the neural response to SCS relevant to paresthesia and analgesia. Specifically, we modeled the dorsal column (DC) axonal response, dorsal column nucleus (DCN) synaptic transmission, conduction failure within DC fiber collaterals, and dorsal horn network output. Importantly, we found that high-frequency stimulation reduces DC fiber activation thresholds, which in turn accurately predicts clinical paresthesia perception thresholds. Furthermore, we show that high-frequency SCS produces asynchronous DC fiber spiking and ultimately asynchronous DCN output, offering a plausible biophysical basis for why high-frequency SCS is less comfortable and produces qualitatively different sensation than low-frequency stimulation. Finally, we demonstrate that model dorsal horn network output is sensitive to SCS-inherent variations in spike timing, which could contribute to heterogeneous pain relief across patients. Importantly, we show that model DC fiber collaterals cannot reliably follow high-frequency stimulation, strongly affecting network output and typically producing anti-nociceptive effects at high frequencies. Altogether, these findings clarify how SCS affects the nervous system and provide insight into the biophysics of paresthesia generation and pain relief.Significance Statement The effects of spinal cord stimulation (SCS) on the nervous system are poorly understood, resulting in inadequate clinical success rates. Here, we use a biophysical modeling approach to investigate the neural response to SCS. We demonstrate that low- and high-frequency stimulation produce contrasting responses in the dorsal columns, brainstem, and dorsal horn. Importantly, our modeling approach was able to accurately predict clinical paresthesia thresholds as a function of frequency, as well as provide plausible biophysical explanations for frequency-dependent effects on paresthesia quality and pain relief. Overall, our results greatly enhance our understanding of the neural response to SCS, thereby offering context for interpreting clinical observations and crucial insight for development of future SCS systems.

Finite Element Analysis of Anterior Implant-Supported Restorations with Different CAD-CAM Restorative Materials.

Due to the lack of literature concerning the selection of crown materials for the restoration of anterior teeth, this study aimed to investigate the effects of six distinct computer-aided design and computer-aided manufacturing (CAD-CAM) crown materials on stress and strain distribution within implant-supported maxillary central incisor restorations, employing finite element analysis (FEA). Furthermore, a comparative analysis was conducted between models that incorporated adjacent natural teeth and those that did not, intending to guide the selection of the most suitable modeling approach. Crown materials, including Lava Ultimate, Enamic, Emax CAD, Suprinity, Celtra Duo, and Cercon xt ML, were the subjects of the investigation. FEA models incorporating Coulomb friction were developed. These models were subjected to an oblique load, simulating the average maximum bite force experienced by anterior teeth. The potential for failure in titanium implant components and the prosthesis crown was evaluated through von Mises and principal stress, respectively. Furthermore, the failure of crestal bone was assessed through principal strain values. Stress values for each implant component and strain values of the bone were extracted from the models. To assess the impact of the six groups of crown materials, Kruskal-Wallis analysis of variance and post-hoc comparisons were conducted. Additionally, a statistical comparison between the two groups with Lava Ultimate and Cercon xt ML was performed using the Mann-Whitney U test to determine the difference in the two modeling approaches. Higher crown material stiffness led to decreased stress in the abutment, fixture, and retaining screw, along with reduced strain in the surrounding bone. However, the decrease in stress and strain values became less significant with increasing crown stiffness. Additionally, the model with adjacent teeth showed significantly lower stress and strain concentrations compared to the model without adjacent teeth. Crowns with a high elastic modulus were the optimal choice for anterior teeth restoration. Constructing FEA models with adjacent teeth was highly recommended to gain a deeper understanding of the mechanical behavior of dental implant restorations.

Simultaneous Water Sorption and Crystallization in ASDs 2: Modeling Long-Term Stabilities.

The physical stability of amorphous solid dispersions (ASDs) is a major topic in the formulation research of oral dosage forms. To minimize the effort of investigating the long-term stability using cost- and time-consuming experiments, we developed a thermodynamic and kinetic modeling framework to predict and understand the crystallization kinetics of ASDs during long-term storage below the glass transition. Since crystallization of the active phrarmaceutical ingredients (APIs) in ASDs largely depends on the amount of water absorbed by the ASDs, water-sorption kinetics and API-crystallization kinetics were considered simultaneously. The developed modeling approach allows prediction of the time evolution of viscosity, supersaturation, and crystallinity as a function of drug load, relative humidity, and temperature. It was applied and evaluated against two-year-lasting crystallization experiments of ASDs containing nifedipine and copovidone or HPMCAS measured in part I of this work. We could show that the proposed modeling approach is able to describe the interplay between water sorption and API crystallization and to predict long-term stabilities of ASDs just based on short-term measurements. Most importantly, it enables explaining and understanding the reasons for different and sometimes even unexpected crystallization behaviors of ASDs.