Model Structure Research Articles

Use of Risk Management to Support Business Sustainability in the Automotive Industry

Today’s companies operate in a dynamic, constantly evolving, and highly competitive environment. The globalization of markets has significantly changed the economy, where companies operate within increasingly complex supply chains. The ever-increasing expectations of customers and company stakeholders, as well as the need to incorporate a comprehensive approach to the life cycle of manufactured products in corporate strategies, expose companies to a whole range of risks. The research was based on the need of organizations operating in the (automotive) industry to manage the dynamics of the business environment. This was accomplished using an appropriate model that, through its universality, would help to ensure the effective risk management in a holistic approach, protecting their performance and meeting the needs of the relevant stakeholders. The main idea of the research was to create and implement a dynamic model of risk management in the environment of a production organization based on the use of available methods such as SWOT, PESTLE, brainstorming, affinity diagrams, risk matrix, SIPOC diagram, risk, and the results of questionnaire surveys. The research was conducted in two directions: (1) designing the structure of the dynamic risk management model for the strategic management process; and (2) verifying the effectiveness of the proposed model in specific cases and evaluating the technical and economic benefits. To support the dynamics of the model, three basic management tools have been proposed: process review, internal audit, and management review, enriched with features that support the concept of risk-based thinking.

Robust Control of Motion in Presence of Uncertain Parameters and Control Constraints

The paper describes a novel, simple servo drive position controller, using only knowledge about the structure of the nonlinear model and the constraints met by individual components of the model. The desired behaviour of the position and velocity signals is obtained by imposing a time-varying constraint on the signal aggregating information about position and velocity tracking errors. The method allows you to determine the maximum control (servo drive current) necessary to achieve the control goal under the existing initial conditions and the selected reference trajectory. The control is constrained and consists in appropriate reaction when the trajectory approaches the barrier, the shape of which is responsible for the imposed properties of the transient and quasisteady state tracking error. In addition to the derivation of the control, a discussion of its possible variants and basic properties is presented. Control with time-varying constraints has been introduced, which allows the control objectives to be met with limited conservatism of the imposed constraints. The influence of technical factors related to actual speed and position measurements was discussed and the operation of the real drive on a laboratory stand was presented.

VoteDroid: a new ensemble voting classifier for malware detection based on fine-tuned deep learning models

AbstractIn this work, VoteDroid a novel fine-tuned deep learning models-based ensemble voting classifier has been proposed for detecting malicious behavior in Android applications. To this end, we proposed adopting the random search optimization algorithm for deciding the structure of the models used as voter classifiers in the ensemble classifier. We specified the potential components that can be used in each model and left the random search algorithm taking a decision about the structure of the model including the number of each component that should be used and its location in the structure. This optimization method has been used to build three different deep learning models namely CNN-ANN, pure CNN, and pure ANN. After selecting the best structure for each DL model, the selected three models have been trained and tested using the constructed image dataset. Afterward, we suggested hybridizing the fine-tuned three deep-learning models to form one ensemble voting classifier with two different working modes namely MMR (Malware Minority Rule) and LMR (Label Majority Rule). To our knowledge, this is the first time that an ensemble classifier has been fine-tuned and hybridized in this way for malware detection. The results showed that the proposed models were promising, where the classification accuracy exceeded 97% in all experiments.

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future.

Using Unmanned Aerial Vehicles and Multispectral Sensors to Model Forage Yield for Grasses of Semiarid Landscapes

Forage yield estimates provide relevant information to manage and quantify ecosystem services in grasslands. We fitted and validated prediction models of forage yield for several prominent grasses used in restoration projects in semiarid areas. We used field forage harvests from three different sites in Northern Utah and Southern California, USA, in conjunction with multispectral, high-resolution UAV imagery. Different model structures were tested with simple models using a unique predictor, the forage volumetric 3D space, and more complex models, where RGB, red edge, and near-infrared spectral bands and associated vegetation indices were used as predictors. We found that for most dense canopy grasses, using a simple linear model structure could explain most (R2 0.7) of the variability of the response variable. This was not the case for sparse canopy grasses, where a full multispectral dataset and a non-parametric model approach (random forest) were required to obtain a maximum R2 of 0.53. We developed transparent protocols to model forage yield where, in most circumstances, acceptable results could be obtained with affordable RGB sensors and UAV platforms. This is important as users can obtain rapid estimates with inexpensive sensors for most of the grasses included in this study.

EGame-flow: psychometric properties of the scale in the Mexican context

PurposeThe present study examined the psychometric properties of the EGame- flow scale in a Mexican sample, presenting evidence of construct validity (exploratory factor analysis and confirmatory factor analysis), reliability (Cronbach’s alpha and McDonald’s omega) and discriminant validity (mean variance extracted).Design/methodology/approachParticipants: Of the 255 Mexican participants in the non-probabilistic sample who had previously interacted with the LOST logistics simulator, 166 (65%) were men and 89 (35%) were women; their ages ranged from 22 to 45. The statistical packages SPSS 25, JASP 0.16 and AMOS 23 facilitated the corresponding analyses. First, we calculated the means and standard deviations of the scale items. Next, we performed an exploratory factor analysis to examine the measurement model’s internal structure and a confirmatory factor analysis to confirm the structure proposed in the exploratory factor analysis. To analyze the internal structure of the measurement model so that the estimates were not affected by multivariate normality problems, we utilized the AMOS bootstrap method (with 500 repetitions, 95% CI), the maximum likelihood (MV) estimation method, and the fit indices: X2, p (chi-square and associated likelihood), Tucker–Lewis index (TLI), standardized statistical mean square residual (SRMR), comparative fit index (CFI) and root mean Square error approximation (RMSEA) with its confidence interval, the values of X2 with p < 0.001; TLI, CFI, AGFI = 0.95; RMSEA and SRMR = 0.08 (Byrne, 2016). Finally, we estimated the reliability of the measurement model with Cronbach’s alpha (a), McDonald’s omega (ω) coefficient and the mean variance extracted (VME).FindingsAn exploratory factor analysis with the MV method and obliminal rotation showed a good fit of the data to the model, which aligns with the significance of the Barlette sphericity test (X2 = 8443.2, p < 0.000) and the Kaiser–Meyer-Olkin (KMO) value of 0.94. The indices confirmed the fit of the data to the six-dimensional model for measuring the users' level of enjoyment of online games (X2 = 678.2 gl = 411, p = 0.000; SRMR = 0.05; TLI = 0.95, CFI = 0.95 and RMSEA = 0.05, IC 90% [0.04, 0.05]).Research limitations/implicationsThe self-reporting format of the scale increases the social desirability of the responses, but the sample only collects information from a specific geographic location, so these findings cannot extrapolate to populations with very marked cultural differences. Finally, the study did not measure other validity evidence, such as predictive and concurrent validity, which should be considered in future studies.Practical implicationsFrom a practical perspective, the study offers a measurement scale with fewer items and robust psychometric evidence that ensures the fit of the data to the EGame-flow measurement scale. Further research must continue to learn about the behavior of the EGame-flow scale in different samples that new evidence of psychometric properties continues to appear and that other factors associated with the users' gaming enjoyment experience are analyzed.Originality/valueThe value and originality of the study lie in the type of evidence of psychometric properties that the instrument has and particularly in the style of sample in which the study is carried out, in this case, in the context of Mexico, where there are not enough instruments that measure the flow experience of users.

CP factor model for dynamic tensors

Abstract Observations in various applications are frequently represented as a time series of multidimensional arrays, called tensor time series, preserving the inherent multidimensional structure. In this paper, we present a factor model approach, in a form similar to tensor CANDECOMP/PARAFAC (CP) decomposition, to the analysis of high-dimensional dynamic tensor time series. As the loading vectors are uniquely defined but not necessarily orthogonal, it is significantly different from the existing tensor factor models based on Tucker-type tensor decomposition. The model structure allows for a set of uncorrelated one-dimensional latent dynamic factor processes, making it much more convenient to study the underlying dynamics of the time series. A new high-order projection estimator is proposed for such a factor model, utilizing the special structure and the idea of the higher order orthogonal iteration procedures commonly used in Tucker-type tensor factor model and general tensor CP decomposition procedures. Theoretical investigation provides statistical error bounds for the proposed methods, which shows the significant advantage of utilizing the special model structure. Simulation study is conducted to further demonstrate the finite sample properties of the estimators. Real data application is used to illustrate the model and its interpretations.

Comparative study for the performance of pure artificial intelligence software sensor and self-organizing map assisted software sensor in predicting 5-day biochemical oxygen demand for Kauma Sewage Treatment Plant effluent in Malawi

Introduction: Modeling plays a crucial role in understanding wastewater treatment processes, yet conventional deterministic models face challenges due to complexity and uncertainty. Artificial intelligence offers an alternative, requiring no prior system knowledge. This study tested the reliability of the Adaptive Fuzzy Inference System (ANFIS), an artificial intelligence algorithm that integrates both neural networks and fuzzy logic principles, to predict effluent Biochemical Oxygen Demand. An important indicator of organic pollution in wastewater.Materials and Methods: The ANFIS models were developed and validated with historical wastewater quality data for the Kauma Sewage Treatment Plant located in Lilongwe City, Malawi. A Self Organizing Map (SOM) was applied to extract features of the raw data to enhance the performance of ANFIS. Cost-effective, quicker, and easier-to-measure variables were selected as possible predictors while using their respective correlations with effluent. Influents’ temperature, pH, dissolved oxygen, and effluent chemical oxygen demand were among the model predictors.Results and Discussions: The comparative results demonstrated that for the same model structure, the ANFIS model achieved correlation coefficients (R) of 0.92, 0.90, and 0.81 during training, testing, and validation respectively, whereas the SOM-assisted ANFIS Model achieved R Values of 0.99, 0.87 and 0.94. Overall, despite the slight decrease in R-value during the testing stage, the SOM- assisted ANFIS model outperformed the traditional ANFIS model in terms of predictive capability. A graphic user interface was developed to improve user interaction and friendliness of the developed model. Integration of the developed model with supervisory control and data acquisition system is recommended. The study also recommends widening the application of the developed model, by retraining it with data from other wastewater treatment facilities and rivers in Malawi.

Development of interpretable machine learning models to predict in-hospital prognosis of acute heart failure patients.

In recent years, there has been remarkable development in machine learning (ML) models, showing a trend towards high prediction performance. ML models with high prediction performance often become structurally complex and are frequently perceived as black boxes, hindering intuitive interpretation of the prediction results. We aimed to develop ML models with high prediction performance, interpretability, and superior risk stratification to predict in-hospital mortality and worsening heart failure (WHF) in patients with acute heart failure (AHF). Based on the Kyoto Congestive Heart Failure registry, which enrolled 4056 patients with AHF, we developed prediction models for in-hospital mortality and WHF using information obtained on the first day of admission (demographics, physical examination, blood test results, etc.). After excluding 16 patients who died on the first or second day of admission, the original dataset (n=4040) was split 4:1 into training (n=3232) and test datasets (n=808). Based on the training dataset, we developed three types of prediction models: (i) the classification and regression trees (CART) model; (ii) the random forest (RF) model; and (iii) the extreme gradient boosting (XGBoost) model. The performance of each model was evaluated using the test dataset, based on metrics including sensitivity, specificity, area under the receiver operating characteristic curve (AUC), Brier score, and calibration slope. For the complex structure of the XGBoost model, we performed SHapley Additive exPlanations (SHAP) analysis, classifying patients into interpretable clusters. In the original dataset, the proportion of females was 44.8% (1809/4040), and the average age was 77.9±12.0. The in-hospital mortality rate was 6.3% (255/4040) and the WHF rate was 22.3% (900/4040) in the total study population. In the in-hospital mortality prediction, the AUC for the XGBoost model was 0.816 [95% confidence interval (CI): 0.815-0.818], surpassing the AUC values for the CART model (0.683, 95% CI: 0.680-0.685) and the RF model (0.755, 95% CI: 0.753-0.757). Similarly, in the WHF prediction, the AUC for the XGBoost model was 0.766 (95% CI: 0.765-0.768), outperforming the AUC values for the CART model (0.688, 95% CI: 0.686-0.689) and the RF model (0.713, 95% CI: 0.711-0.714). In the XGBoost model, interpretable clusters were formed, and the rates of in-hospital mortality and WHF were similar among each cluster in both the training and test datasets. The XGBoost models with SHAP analysis provide high prediction performance, interpretability, and reproducible risk stratification for in-hospital mortality and WHF for patients with AHF.

Shaking Tables Test on Seismic Responses of a Long-Span Rigid-Framed Bridge Considering Traveling Wave Effect and Soil–Structure Interaction

The traveling wave effect and soil–structure interaction have significant influence on the seismic response of large-span bridges with complex site conditions. In this paper, a 1/10 scaled-down large-span rigid-framed bridge model was designed and fabricated, and a shaking tables test considering the traveling wave effect and soil–structure interaction was carried out on a large-scale continuous rigid bridge model by a real-time substructure hybrid test technique. Influences of the traveling wave effect and soil–structure interaction on the seismic responses of the rigid-framed bridge specimen were systematically analyzed with experimental data. The test results showed that when the apparent wave speed was small, the traveling wave effect increased the seismic responses of the rigid-framed bridge. With the increase in apparent wave speed, the structural response under traveling wave excitation and uniform excitation was basically the same. The SSI effect lead to a great change in the seismic input peaks and spectral characters at the bottom of the pier, and increased the seismic responses of the rigid-framed bridge. When both traveling wave and the SSI effect were considered, there was a phase difference in the seismic excitation. The dynamic responses of a continuous rigid-framed bridge could not be simply obtained by superposition of the separate traveling wave effect or SSI effect. Meanwhile, the real-time substructure test method in this paper solved the problems that the traditional soil box experiment cannot be applied to the test of a large-scale model, the soil and bridge structure find it difficult to meet the unified similarity ratio, and the boundary conditions are difficult to simulate accurately.

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

In recent years, swarm intelligence optimization algorithms have been proven to have significant effects in solving combinatorial optimization problems. Introducing the concept of evolutionary computing, which is currently a hot research topic, into swarm intelligence optimization algorithms to form novel swarm intelligence optimization algorithms has proposed a new research direction for better solving combinatorial optimization problems. The longhorn beetle whisker search algorithm is an emerging heuristic algorithm, which originates from the simulation of longhorn beetle foraging behavior. This algorithm simulates the touch strategy required by longhorn beetles during foraging, and achieves efficient search in complex problem spaces through bioheuristic methods. This article reviews the research progress on the search algorithm for longhorn beetles from 2017 to present. Firstly, the basic principle and model structure of the beetle whisker search algorithm were introduced, and its differences and connections with other heuristic algorithms were analyzed. Secondly, this paper summarizes the research achievements of scholars in recent years on the improvement of longhorn whisker search algorithms. Then, the application of the beetle whisker search algorithm in various fields was explored, including function optimization, engineering design, and path planning. Finally, this paper summarizes the research achievements of scholars in recent years on the improvement of the longhorn whisker search algorithm, and proposes future research directions, including algorithm deep learning fusion, processing of multimodal problems, etc. Through this review, readers will have a comprehensive understanding of the research status and prospects of the longhorn whisker search algorithm, providing useful guidance for its application in practical problems.

Insights into the Mechanism of Catalytic Activity of Plasmodium Parasite Malate-Quinone Oxidoreductase.

Plasmodium malate-quinone oxidoreductase (MQO) is a membrane flavoprotein catalyzing the oxidation of malate to oxaloacetate and the reduction of quinone to quinol. Recently, using a yeast expression system, we demonstrated that MQO, expressed in place of mitochondrial malate dehydrogenase (MDH), contributes to the TCA cycle and the electron transport chain in mitochondria, making MQO attractive as a promising drug target in Plasmodium malaria parasites, which lack mitochondrial MDH. However, there is little information on the structure of MQO and its catalytic mechanism, information that will be required to develop novel drugs. Here, we investigated the catalytic site of P. falciparum MQO (PfMQO) using our yeast expression system. We generated a model structure for PfMQO with the AI tool AlphaFold and used protein footprinting by acetylation with acetic anhydride to analyze the surface topology of the model, confirming the computational prediction to be reasonably accurate. Moreover, a putative catalytic site, which includes a possible flavin-binding site, was identified by this combination of protein footprinting and structural prediction model. This active site was analyzed by site-directed mutagenesis. By measuring enzyme activity and protein expression levels in the PfMQO mutants, we showed that several residues at the active site are essential for enzyme function. In addition, a single substitution mutation near the catalytic site resulted in enhanced sensitivity to ferulenol, an inhibitor of PfMQO that competes with malate for binding to the enzyme. This strongly supports the notion that the substrate binds to the proposed catalytic site. Then, the location of the catalytic site was demonstrated by structural comparison with a homologous enzyme. Finally, we used our results to propose a mechanism for the catalytic activity of MQO by reference to the mechanism of action of structurally or functionally homologous enzymes.

Flat model structures and Gorenstein objects in functor categories

We construct a flat model structure on the category ${_{\mathcal {Q},\,R}\mathsf {Mod}}$ of additive functors from a small preadditive category $\mathcal {Q}$ satisfying certain conditions to the module category ${_{R}\mathsf {Mod}}$ over an associative ring $R$ , whose homotopy category is the $\mathcal {Q}$ -shaped derived category introduced by Holm and Jørgensen. Moreover, we prove that for an arbitrary associative ring $R$ , an object in ${_{\mathcal {Q},\,R}\mathsf {Mod}}$ is Gorenstein projective (resp., Gorenstein injective, Gorenstein flat, projective coresolving Gorenstein flat) if and only if so is its value on each object of $\mathcal {Q}$ , and hence improve a result by Dell'Ambrogio, Stevenson and Šťovíček.

Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation.

Protein aggregation is behind the genesis of incurable diseases and imposes constraints on drug discovery and the industrial production and formulation of proteins. Over the years, we have been advancing the Aggresscan3D (A3D) method, aiming to deepen our comprehension of protein aggregation and assist the engineering of protein solubility. Since its inception, A3D has become one of the most popular structure-based aggregation predictors because of its performance, modular functionalities, RESTful service for extensive screenings, and intuitive user interface. Building on this foundation, we introduce Aggrescan4D (A4D), significantly extending A3D's functionality. A4D is aimed at predicting the pH-dependent aggregation of protein structures, and features an evolutionary-informed automatic mutation protocol to engineer protein solubility without compromising structure and stability. It also integrates precalculated results for the nearly 500,000 jobs in the A3D Model Organisms Database and structure retrieval from the AlphaFold database. Globally, A4D constitutes a comprehensive tool for understanding, predicting, and designing solutions for specific protein aggregation challenges. The A4D web server and extensive documentation are available at https://biocomp.chem.uw.edu.pl/a4d/. This website is free and open to all users without a login requirement.

The role of digital literacy, epistemological belief and reading motivation and engagement in teaching 21st century skills

PurposeThe aim of this study was to shed some light on the extent to which digital literacy, epistemological belief and reading motivation and engagement predict pre-service teachers’ capabilities in teaching 21st century skills.Design/methodology/approach21st Century Skills Teaching Scale, Internet based Epistemological Beliefs Scale, Internet based Reading Motivation and Engagement Scale as well as Digital Literacy Scale were used to collect the data. A total of 512 pre-service teachers from four universities in Türkiye participated in the study. Descriptive statistics, correlation and structural equation model fitness tests were performed by using SPSS 24.0 and AMOS 22.0.FindingsFirst, the results of the correlations analysis indicated that there were statistically significant positive correlations among measured variables. Second, the results of the path and SEM model fit tests demonstrated that all the model fit indices of X2/df, TLI, CFI, RMSEA met the acceptable criteria and were shown to be an adequate model structure. Finally, the results demonstrated strong evidence for predicting effects of digital literacy, epistemological belief as well as reading motivation and engagement on teaching 21st century skills.Research limitations/implicationsThis study is limited by the fact that the evidence relies solely on pre-service teachers' self-reporting. Secondly, although the hypothesised model explained 45.2% variance of pre-service teachers’ competence in the 21st century skills, 54.8% of it remains unexplained.Originality/valueThe results of this study suggest important implications for teaching programs as they underscore the importance of digital literacy on epistemological beliefs, reading motivation and competence in teaching 21st century skills.

Digital marketing business models: Key stages, types and trends of development

The main goal of the article is to formalize the key business models of marketing of modern companies and substantiate the key stages, types and trends of development. The relevance and need to pay significant attention to the marketing digital business model when organizing a business is substantiated. Using structural and logical analysis and criticism of scientific research, the essence, advantages and disadvantages are determined, the main blocks, stages and key elements of the structure of business models of modern companies are argued. It has been proven that marketing digital business models serve as a logical and visual plan for organizing all business processes of companies from production, marketing, sales and logistics to building a hierarchy of profitability. The key development trends are substantiated and the most popular business models of business organization in modern conditions are structured on the basis of scientific generalization, structural and logical analysis and mathematical modeling. Practical significance is characterized by the fact that the marketing business models of world-class companies are generalized and structured, taking into account their specifics and characteristics. Practical recommendations and key stages of building a company’s business model and its implementation into reality have been formed to achieve strategic business goals.

Transformer-based intelligent fault diagnosis methods of mechanical equipment: A survey

Abstract Transformer is extensively employed in natural language processing, and computer vision (CV), with the self-attention structure. Due to its outstanding long-range dependency modeling and parallel computing capability, some leading researchers have recently attempted to apply Transformer to intelligent fault diagnosis tasks for mechanical equipment, and have achieved remarkable results. Physical phenomena such as changes in vibration, sound, and heat play a crucial role in the research of mechanical equipment fault diagnosis, which directly reflects the operational status and potential faults of mechanical equipment. Currently, intelligent fault diagnosis of mechanical equipment based on monitoring signals such as vibration, sound, and temperature using Transformer-based models remains a popular research topic. While some review literature has explored the related principles and application scenarios of Transformer, there is still a lack of research on its application in intelligent fault diagnosis tasks for mechanical equipment. Therefore, this work begins by examining the current research status of fault diagnosis methods for mechanical equipment. This study first provides a brief overview of the development history of Transformer, outlines its basic structure and principles, and analyzes the characteristics and advantages of its model structure. Next it focuses on three model variants of Transformer that have generated a significant impact in the field of CV. Following that, the research progress and current challenges of Transformer-based intelligent fault diagnosis methods for mechanical equipment are discussed. Finally, the future development direction of Transformer in the field of mechanical equipment fault diagnosis is proposed.

Developing Parametric and Nonparametric Models for Model-Informed Precision Dosing: A Quality Improvement Effort in Vancomycin for Patients With Obesity.

Both parametric and nonparametric methods have been proposed to support model-informed precision dosing (MIPD). However, which approach leads to better models remains uncertain. Using open-source software, these 2 statistical approaches for model development were compared using the pharmacokinetics of vancomycin in a challenging subpopulation of class 3 obesity. Patients on vancomycin at the University of Vermont Medical Center from November 1, 2021, to February 14, 2023, were entered into the MIPD software. The inclusion criteria were body mass index (BMI) of at least 40 kg/m2 and 1 or more vancomycin levels. A parametric model was created using nlmixr2/NONMEM, and a nonparametric model was created using metrics. Then, a priori and a posteriori predictions were evaluated using the normalized root mean squared error (nRMSE) for precision and the mean percentage error (MPE) for bias. The parametric model was evaluated in a simulated MIPD context using an external validation dataset. In total, 83 patients were included in the model development, with a median age of 56.6 years (range: 24-89 years), and a median BMI of 46.3 kg/m2 (range: 40-70.3 kg/m2). Both parametric and nonparametric models were 2-compartmental, with creatinine clearance and fat-free mass as covariates to c clearance and volume parameters, respectively. The a priori MPE and nRMSE for the parametric versus nonparametric models were -6.3% versus 2.69% and 27.2% versus 30.7%, respectively. The a posteriori MPE and RMSE were 0.16% and 0.84%, and 13.8% and 13.1%. The parametric model matched or outperformed previously published models on an external validation dataset (n = 576 patients). Minimal differences were found in the model structure and predictive error between the parametric and nonparametric approaches for modeling vancomycin class 3 obesity. However, the parametric model outperformed several other models, suggesting that institution-specific models may improve pharmacokinetics management.

Evaluating structure and content of parent-implemented early logopaedic intervention models following the three stages of communicative development in children with cleft lip and/or palate: Systematic literature review with narrative synthesis.

The development of communication, speech and language follows three stages (development of the parent-child relationship, interactions and actual speech and language acquisition). Children born with cleft lip and/or palate are at increased risk of communicative problems while parents may be going through an emotionally difficult time. Early parent-implemented logopaedic intervention that supports both parents and child is important. Three systematic reviews have examined the effects of early speech and language interventions, but not their structure and content. To investigate which early parent-implemented logopaedic interventions already exist for children with cleft lip and/or palate, and to evaluate their structure, content and time of onset against the three stages of communicative development. Six databases (PubMed, Embase, Web of Science, APA PsycInfo, Cinahl and Scopus) were searched between inception and 31 March 2023 to identify published articles that reported early parent-implemented logopaedic interventions in children with cleft lip and/or palate, aged 0 to 3 years, clearly describing the strategies used to train parents. Two authors independently assessed the eligibility of the studies. Quality assessment was conducted using the Physiotherapy Evidence Database quality assessment tool, Single-Case Experimental Design tool and the National Institutes of Health pre-post-study tools. The structure and content of the interventions were analysed taking into account the needs and difficulties of both the parents and the child according to the three stages of communicative development. The systematic literature search identified four studies that met the inclusion criteria. Three of them had a Level of Evidence III and one study had a Level of Evidence IV. Strategies appropriate for Stage 1 of communicative development (parent-child relationship) are well represented in only one study, but the psychosocial needs of parents are currently not included in these programmes. However, research shows that parental emotional difficulties can adversely impact a child's communicative development. Strategies appropriate for Stage 2 (promoting social interactions) are better represented. However, strategies appropriate for Stage 3 (acquiring correct speech and language patterns) are most represented in all intervention programmes. Three out of four intervention programmes focus on Stage 3 (actual speech and language stimulation). Stage 1 is underrepresented and the psychosocial needs of parents are currently not included in existing intervention programmes. Further research is needed in close collaboration with psychologists to construct a comprehensive, longitudinal, developmentally appropriate intervention programme that equally represents the three stages of communicative development and considers the psychosocial needs of parents. What is already known on the subject Children with cleft lip and/or palate are at increased risk of speech and language problems from birth. Parents of these children often have emotional problems following their child's diagnosis. The effectiveness of early intervention to facilitate the child's speech and language development has already been proven. Early intervention is recommended for both parents and child, but little is known about early parent-implemented logopaedic interventions that also provide psychosocial support for parents. What this paper adds to existing knowledge This review has shown that existing early parent-implemented logopaedic interventions for children with cleft lip and/or palate focus mainly on facilitating responsive interactions and actual speech and language development (Stages 2 and 3 of communicative development). However, Stage 1, where the parent-child relationship develops, is currently not included, even though this stage is a prerequisite of subsequent stages. If parents are struggling with emotional problems (following their child's diagnosis) this can negatively impact their mental health, the parent-child relationship, attachment and their child's development. What are the potential or actual clinical implications of this work? A clinical implication of the findings in this review is that more attention should be paid to Stage 1 of communicative development in early parent-implemented logopaedic interventions. By working closely with the psychologist of the cleft (and craniofacial) team, any psychosocial needs of the parents can be included in the counselling. As a result, the parents and their child are seen and supported as a unit and the parent-child relationship can develop optimally.

Hydrogen evolution descriptors of 55-atom PtNi nanoclusters and interaction with graphite

Density functional simulations have been performed for Pt n Ni 55−n clusters ( n=0,12,20,28,42,55 ) to investigate their catalytic properties for the hydrogen evolution reaction (HER). Starting from the icosahedral Pt12Ni43 , hydrogen adsorption energetics and electronic d-band descriptors indicate HER activity comparable to that of pure Pt55 (distorted reduced core structure). The PtNi clusters accommodate a large number of adsorbed hydrogen before reaching a saturated coverage, corresponding to 3–4 H atoms per icosahedron facet (in total ∼70–80). The differential adsorption free energies are well within the window of |ΔGH|<0.1 eV which is considered optimal for HER. The electronic descriptors show similarities with the platinum d-band, although the uncovered PtNi clusters are magnetic. Increasing hydrogen coverage suppresses magnetism and depletes electron density, resulting in expansion of the PtNi clusters. For a single H atom, the adsorption free energy varies between −0.32 ( Pt12Ni43 ) and −0.59 eV ( Pt55 ). The most stable adsorption site is Pt–Pt bridge for Pt-rich compositions and a hollow site surrounded by three Ni for Pt-poor compositions. A hydrogen molecule dissociates spontaneously on the Pt-rich clusters. The above HER activity predictions can be extended to PtNi on carbon support as the interaction with a graphite model structure (w/o vacancy defect) results in minor changes in the cluster properties only. The cluster-surface interaction is the strongest for Pt55 due to its large facing facet and associated van der Waals forces.