Alternative Models Research Articles

A triangular model of fractal growth with application to adsorptive spin-coating of polymers.

Over the last 40 years, applied mathematicians and physicists have proposed a number of mathematical models that produce structures exhibiting a fractal dimension. This work has coincided with the discovery that objects with fractal dimension are relatively common in the natural and human-produced worlds. One particularly successful model of fractal growth is the diffusion limited aggregation (DLA) model, a model as notable for its simplicity as for its complex and varied behavior. It has been modified and used to simulate fractal growth processes in numerous experimental and empirical contexts. In this work, we present an alternative fractal growth model that is based on a growing mass that bonds to particles in a surrounding medium and then exerts a force on them in an iterative process of growth and contraction. The resulting structure is a spreading triangular network rather than an aggregate of spheres, and the model is conceptually straightforward. To the best of our knowledge, this model is unique and differs in its dynamics and behavior from the DLA model and related particle aggregation models. We explore the behavior of the model, demonstrate the range of model output, and show that model output can have a variable fractal dimension between 1.5 and 1.83 that depends on model parameters. We also apply the model to simulating the development of polymer thin films prepared using spin-coating which also exhibit variable fractal dimensions. We demonstrate how the model can be adjusted to different dewetting conditions as well as how it can be used to simulate the modification of the polymer morphology under solvent annealing.

Prescription eyeglasses as a forensic physical evidence: Prediction of age based on refractive error measures using machine learning algorithm.

Refractive errors (RE) are commonly reported visual impairment problems worldwide. Previous clinical studies demonstrated age-related changes in human eyes. We hypothesized that the binocular RE metrics including sphere and cylinder power, axis orientation, and interpupillary distance (IPD) can be used for forensic age estimation of an unknown individual. RE data of both eyes were collected from the clinical optometric exams and prescription glasses of 2027 Egyptian individuals aged between 2 to 93 years. The differences between age groups as well as sides, and sexual dimorphism were explored. Two modeling methods were compared: multiple and stepwise linear regression (LR) versus machine learning Regression Forest (RFM). Data were apportioned into training and test datasets with a ratio of 80/20. The results showed significant differences among the age groups in each eye for all variables. Stepwise LR improved the results over models based on the one-sided lens due to selection of IPD in addition to the left and right axis, and left sphere as independent variables. For the RFM, the left axis and IPD were the most important features. RFM outperformed LR in terms of accuracy and root mean squared error (RMSE). The estimated age within ±10 years showed 81.4% accuracy rate and RMSE = 8.9 years versus 38.5% accuracy rate and RMSE = 17.99 years using RFM and stepwise LR, respectively, in the test set. The current study upholds the significance of the age-related changes of refractive error in formulating alternative forensic age estimation models when standard methods are unavailable.

Timing the transition: gender diversity’s role in family IPOs

PurposeThis study investigates the relationship between board gender diversity (BGD) and the time to Initial Public Offering (IPO), which stands as an entrepreneurially risky choice, particularly challenging in family firms. We also investigate the moderating role of family ownership dispersion (FOD).Design/methodology/approachWe draw on an integrated theoretical framework bringing together the upper echelons theory and the socio-emotional wealth (SEW) perspective and on hand-collected data on a sample of Italian family IPOs that occurred in the period 2000–2020. We employ ordinary least squares (OLS) regression and alternative model estimations to test our hypotheses.FindingsBGD positively affects the time to IPO, thus, it increases the time required to go public. FOD negatively moderates this relationship. Our findings remain robust with different measures for BGD, FOD, and family business definition as well as with different econometric models.Originality/valueThe article develops literature on family firms and IPO and it enriches the academic debate about gender and IPOs in family firms. It adds to studies addressing the determinants of the time to IPO by incorporating gender diversity and the FOD into the discussion. Finally, it contributes to research on women and outcomes in family firms.

The hidden risk: Changes in functional potentials of microbial keystone taxa under global climate change jeopardizing soil carbon storage in alpine grasslands

Climate change is endangering the soil carbon stock of alpine grasslands on the Qinghai-Tibetan Plateau (QTP), but the limited comprehension regarding the mechanisms that sustain carbon storage under hydrothermal changes increases the uncertainty associated with this finding. Here, we examined the relative abundance of soil microbial keystone taxa and their functional potentials, as well as their influence on soil carbon storage with increased precipitation across alpine grasslands on the QTP, China. The findings indicate that alterations in precipitation significantly decreased the relative abundance of the carbon degradation potentials of keystone taxa, such as chemoheterotrophs. The inclusion of keystone taxa and their internal functional potentials in the two best alternative models explained 70% and 63% of the variance in soil organic carbon (SOC) density, respectively. Moreover, we found that changes in chemoheterotrophs had negative effects on SOC density as indicated by a structural equation model, suggesting that some specialized functional potentials of keystone taxa are not conducive to the accumulation of carbon sink. Our study offers valuable insights into the intricate correlation between precipitation-induced alterations in soil microbial keystone taxa and SOC storage, highlighting a rough categorization is difficult to distinguish the hidden threats and the importance of incorporating functional potentials in SOC storage prediction models in response to changing climate.

A comparison of alternative models for solving a non-linear single plant Hydro Unit Commitment problem

A wide range of real world optimization problems involves continuous decisions and non-linearities. Each non-linear component of such problems can be modeled either linearly or non-linearly, considering or not additional integer variables. This results into different modeling choices that can drastically impact the solution time and quality. In this paper, we evaluate representative modeling alternatives, including common models from the literature as well as new models featuring less common functions. The single plant Hydro Unit Commitment problem (1-HUC) is the considered non-linear use case. Among the non-linearities of the 1-HUC, we focus on those involved in the power production, more precisely the head effect and the turbines efficiency. The power is defined as a two-dimensional non-convex and non-concave function of the water flow and head decision variables, the latter being itself a one-dimensional concave function of the turbined volume. We consider both the general problem and a common special case, assuming that the water head is fixed. Several available solvers are used for each non-linear model and the best virtual solver is retained to focus on the model capabilities rather than on the solver performance. Based on the numerical experiments, three models stand out as the most efficient in terms of computational time, solution quality and feasibility, sometimes in a counter-intuitive manner. For each of these models, a solver is highlighted as the most adequate.

Enhancing Fetal Medical Image Analysis through Attention-guided Convolution: A Comparative Study with Established Models

The ability to detect and track fetal growth is greatly aided by medical image analysis, which plays a crucial role in parental care. This study introduces an attention-guided convolutional neural network (AG-CNN) for maternal–fetal ultrasound image analysis, comparing its performance with that of established models (DenseNet 169, ResNet50, and VGG16). AG-CNN, featuring attention mechanisms, demonstrates superior results with a training accuracy of 0.95 and a testing accuracy of 0.94. Comparative analysis reveals AG-CNN’s outperformance against alternative models, with testing accuracies for DenseNet 169 at 0.90, ResNet50 at 0.88, and VGG16 at 0.86. These findings underscore the effectiveness of AG-CNN in fetal image analysis, emphasising the role of attention mechanisms in enhancing model performance. The study’s results contribute to advancing the field of obstetric ultrasound imaging by introducing a novel model with improved accuracy, demonstrating its potential for enhancing diagnostic capabilities in maternal–fetal healthcare.

Threshold nonlinearities and the democracy-growth nexus

Summary This paper investigates the relationship between democracy and economic growth in the context of a linear index threshold regression model. We first introduce the baseline model with endogeneity and propose a two-step smoothed generalised method of moments estimation method. We establish the consistency and derive the asymptotic distributions of the proposed estimators. We then extend the approach to a dynamic panel context and employ the model to explore the impact of democratisation on economic growth. Our findings reveal that democratisation’s impact on growth is nonlinear and depends on the country’s current institutional quality level. Furthermore, democracy’s impact on economic growth is more pronounced in countries with higher education levels than others, suggesting that education also plays a crucial role in enhancing the positive effects of democracy. Our proposed estimator can be used in other situations that require the use of more than one threshold variable. In these cases, our hybrid estimator has less stringent data requirements than an alternative model where the thresholds would enter separately, especially when the threshold variables are correlated.

The maximum length car sequencing problem

This paper introduces the maximum length car sequencing problem to support the assembly operations of a multinational automotive company. We propose an integer linear programming (ILP) formulation to schedule the maximum number of cars without violating the so-called option constraints. In addition, we present valid combinatorial lower and upper bounds, which can be calculated in less than 0.01 s, as well as binary and iterative search algorithms to solve the problem when good primal bounds are not readily available. To quickly obtain high-quality solutions, we devise an effective iterated local search algorithm, and we use the heuristic solutions as warm start to further enhance the performance of the exact methods. Computational results demonstrate that relatively low gaps were achieved for benchmark instances within a time limit of ten minutes. We also conducted an instance space analysis to identify the features that make the problem more difficult to solve. Moreover, the instances reflecting the company’s needs could be solved to optimality in less than a second. Finally, simulations with real-world demands, divided into shifts, were conducted over a period of four months. In this case, we use the proposed ILP model in all shifts except the last one of each month, for which we employ an alternative ILP model to sequence the unscheduled cars, adjusting the pace of the assembly line in an optimal fashion. The results pointed out that the latter was necessary in only one of the months.

Adeno-Associated Virus-Mediated Dorsal Root Ganglion Toxicity in the New Zealand White Rabbit.

Recombinant adeno-associated virus (AAV)-mediated degeneration of sensory neurons in the dorsal root ganglia (DRG) and trigeminal ganglia (TG) has been observed in non-human primates (NHPs) following intravenous (IV) and intrathecal (IT) delivery. Administration of recombinant AAV encoding a human protein transgene via a single intra-cisterna magna (ICM) injection in New Zealand white rabbits resulted in histopathology changes very similar to NHPs: mononuclear cell infiltration, degeneration/necrosis of sensory neurons, and nerve fiber degeneration of sensory tracts in the spinal cord and of multiple nerves. AAV-associated clinical signs and incidence/severity of histologic findings indicated that rabbits were equally or more sensitive than NHPs to sensory neuron damage. Another study using human and rabbit transgene constructs of the same protein demonstrated comparable changes suggesting that the effects are not an immune response to the non-self protein transgene. Rabbit has not been characterized as a species for general toxicity testing of AAV gene therapies, but these studies suggest that it may be an alternative model to investigate mechanisms of AAV-mediated neurotoxicity and test novel AAV designs mitigating these adverse effects.

Model Pembiayaan Agribisnis Berbasis UMKM di Kabupaten Sinjai (Studi Kasus: KUB Anggrek Desa Aska)

Financing in agribusiness, especially in businesses related to making MSME-based banana frond handicrafts, is needed in managing their business. The choice of the right financing model will depend on the needs and conditions of MSMEs. This study aims to analyze and describe the financing model at the Anggrek KUB of Aska Village, South Sinjai District, Sinjai Regency. The sampling method used is purposive sampling method. The results showed that Anggrek KUB of Aska Village currently only uses a financing model from personal capital, the P3AP2KB Office of Sinjai Regency, the UMKM Cooperative and Manpower Office of Sinjai Regency, and the Industry Trade and Energy Mineral Resources Office of Sinjai Regency. Alternative models for financing the Aska Village Orchid KUB include crowdfunding, venture capital, Microfinance Institutions (MFIs), and grants and aid programs.

Is the Comparator in Your Diagnostic Cost-Effectiveness Model “Standard of Care”? Recommendations from Literature Reviews and Expert Interviews on How to Identify and Operationalize It

ObjectivesThis research aimed to develop best-practice recommendations for identifying the “standard of care” (SoC) and integrate it when it is the comparator in diagnostic economic models (SoC comparator). MethodsA multi-methods approach comprising 2 pragmatic literature reviews and 9 expert interviews was used. Experts rated their agreement with draft recommendations based on the authors’ analysis of the reviews. These were refined iteratively to produce final recommendations. ResultsFourteen best-practice recommendations are provided. Care pathway mapping (using quantitative, qualitative, or mixed-methods approaches) should be used for identifying the SoC comparator. Guidelines analysis can be integrated with expert opinion to identify pathway variability and discrepancies from clinical practice. For integrating the SoC comparator into the model, recommendations around structure, input sourcing, data aggregation and reporting, input uncertainty, and model variability are presented. For example, modelers should consider that the reference standard is not synonymous with the SoC, and the SoC may not be the only comparator. The comparator limitations should be discussed with clinical experts, but elicitation of its diagnostic accuracy is not recommended. Probabilistic sensitivity analysis is recommended when evaluating the overall input uncertainty, and deterministic sensitivity analysis is useful when there is high model uncertainty or SoC variability. Consensus could not be reached for some topics (eg, the role of real-world data, model averaging, and alternative model structures), but the reported discussions provide points for consideration. ConclusionsTo our knowledge, this is the first guidance to support modelers when identifying and operationalizing the SoC comparator in diagnostic cost-effectiveness models.

Scaling up open access publishing through transformative agreements: Results from 2019 to 2022

Key Points The Biochemical Society's transformative Read & Publish (R&P) agreements follow an all‐inclusive and unlimited model (developed in collaboration with other society publishers) that cover all titles, both hybrid and fully open access (OA), and does not place caps on article numbers. This case study shows that these R&P agreements have significantly boosted OA uptake in the United Kingdom, Australia and New Zealand. It also shows that the agreements are most effective in regions where there is adequate funding, high research output and a willingness from institutions to engage collectively (through consortium agreements). Where these conditions do not exist alternative models will be needed to complement R&P—in our case we plan to implement Subscribe to Open from 2025 onwards.

Shadows or Forgeries? Explaining Legal Normativity

Abstract Legal norms serve as practical standards for individuals and officials. While this ‘normative aspect’ of law is widely acknowledged, its significance for theories of law remains contested. In this paper, I examine three views on the matter. First, that we should explain legal norms as reason-giving. Second, that we should explain legal discourse as being about reasons for action. Third, that we should explain law as capable of being reason-giving. I survey some challenges associated with each of these views. What they have in common is an implicit assumption about the form that normative explanation must take: that it must be a linear, non-reductive explanation. There is an alternative model for normative explanation available, however. That model explains normative notions in terms of the practices and attitudes involved in recognizing, offering, and demanding them. I highlight the potentials, and limitations, of this practice-centered alternative.

A new performance-based measure of personality functioning impairment: development and preliminary evaluation of reliability and validity

Personality functioning impairment is at the center of many dimensional models of personality. Available measures of personality functioning impairment are limited to self-report, clinician-/informant-rated, and interview methods. Although researchers have begun investigating established performance-based instruments’ potential for assessing personality functioning impairment, administration and scoring of these instruments is complex and the latent variables they measure diverge from personality functioning impairment as described in the ICD-11 and the Alternative Model for Personality Disorders (AMPD) of the DSM. We address this absence by developing and psychometrically evaluating the Level of Personality Functioning Scale-Questionnaire-based Implicit Association Test (LPFS-qIAT). The LPFS-qIAT’s psychometric properties were evaluated across four studies, producing initial evidence supporting the new instrument’s reliability as well as its convergent, discriminant, and criterion-related validity. As the first performance-based measure of personality functioning impairment that aligns with the AMPD and, to a degree the ICD-11, that is easily administered, scored, and interpreted, the LPFS-qIAT shows potential to become a valuable tool in both research and clinical practice.

Human stem cell transplantation models of Alzheimer's disease.

Alzheimer's disease (AD) is the most frequent form of dementia. It is characterized by pronounced neuronal degeneration with formation of neurofibrillary tangles and deposition of amyloid β throughout the central nervous system. Animal models have provided important insights into the pathogenesis of AD and they have shown that different brain cell types including neurons, astrocytes and microglia have important functions in the pathogenesis of AD. However, there are difficulties in translating promising therapeutic observations in mice into clinical application in patients. Alternative models using human cells such as human induced pluripotent stem cells (iPSCs) may provide significant advantages, since they have successfully been used to model disease mechanisms in neurons and in glial cells in neurodegenerative diseases in vitro and in vivo. In this review, we summarize recent studies that describe the transplantation of human iPSC-derived neurons, astrocytes and microglial cells into the forebrain of mice to generate chimeric transplantation models of AD. We also discuss opportunities, challenges and limitations in using differentiated human iPSCs for in vivo disease modeling and their application for biomedical research.

Introjected regulation and academic burnout: A moderated mediation model of social comparison and distress overtolerance.

The primary purpose of this study is to examine the mediation effect of Contrastive Upward Comparison (CUC) on the relationship between introjected regulation and exhaustion (i.e., introjected regulation-CUC-exhaustion). It is also aimed to examine the moderation effect of distress overtolerance on this mediated relationship. In order to resolve the uncertainty about the causality of cross-sectional studies, this study worked on the relationship among the variables by setting an alternative model (i.e., CUC-introjected regulation - exhaustion). The participants were 167 (females, 70.7%) undergraduate and graduate students in South Korea. The results of this study indicated that introjected regulation showed indirect and significant effect on exhaustion via CUC. However, in the alternative model, the indirect effect of introjected regulation in the relationship between CUC and exhaustion was not significant. Furthermore, high distress overtolerance buffered the relationship between introjected regulation and CUC, while it strengthened the association of CUC and exhaustion. Lastly, the results of moderated mediation suggested that students with higher distress overtolerance, experienced more CUC, and emotional exhaustion. The implications and limitations were also discussed.

Feasibility of video-based real-time nystagmus tracking: a lightweight deep learning model approach using ocular object segmentation.

Eye movement tests remain significantly underutilized in emergency departments and primary healthcare units, despite their superior diagnostic sensitivity compared to neuroimaging modalities for the differential diagnosis of acute vertigo. This underutilization may be attributed to a potential lack of awareness regarding these tests and the absence of appropriate tools for detecting nystagmus. This study aimed to develop a nystagmus measurement algorithm using a lightweight deep-learning model that recognizes the ocular regions. The deep learning model was used to segment the eye regions, detect blinking, and determine the pupil center. The model was trained using images extracted from video clips of a clinical battery of eye movement tests and synthesized images reproducing real eye movement scenarios using virtual reality. Each eye image was annotated with segmentation masks of the sclera, iris, and pupil, with gaze vectors of the pupil center for eye tracking. We conducted a comprehensive evaluation of model performance and its execution speeds in comparison to various alternative models using metrics that are suitable for the tasks. The mean Intersection over Union values of the segmentation model ranged from 0.90 to 0.97 for different classes (sclera, iris, and pupil) across types of images (synthetic vs. real-world images). Additionally, the mean absolute error for eye tracking was 0.595 for real-world data and the F1 score for blink detection was ≥ 0.95, which indicates our model is performing at a very high level of accuracy. Execution speed was also the most rapid for ocular object segmentation under the same hardware condition as compared to alternative models. The prediction for horizontal and vertical nystagmus in real eye movement video revealed high accuracy with a strong correlation between the observed and predicted values (r = 0.9949 for horizontal and r = 0.9950 for vertical; both p < 0.05). The potential of our model, which can automatically segment ocular regions and track nystagmus in real time from eye movement videos, holds significant promise for emergency settings or remote intervention within the field of neurotology.

The German PCL-5: evaluating structural validity in a large-scale sample of the general German population

ABSTRACT Background: In attempts to elucidate PTSD, recent factor analytic studies resulted in complex models with a proliferating number of factors that lack psychometrical and clinical utility. Recently, suggestions have been made to optimize factor analytic practices to meet a refined set of statistical and psychometric criteria. Objective: This study aims to assess the factorial structure of the German version of the PCL-5, implementing recent methodological advancements to address the risk of overfitting models. In doing so we diverge from traditional factor analytical research on PTSD. Method: On a large-scale sample of the German general population (n = 1625), exploratory factor analyses were run to investigate the dimensionality found within the data. Subsequently, we validated and compared all model suggestions from our preliminary analyses plus all standard and common alternative PTSD factor models (including the ICD-11 model) from previous literature with confirmatory factor analyses. We not only consider model fit indices based on WLSMV estimation but also deploy criteria such as favouring less complex models with a parsimonious number of factors, sufficient items per factor, low inter-factor correlations and number of model misspecifications. Results: All tested models showed adequate to excellent fit in respect to traditional model fit indices; however, models with two or more factors increasingly failed to meet other statistical and psychometric criteria. Conclusion: Based on the results we favour a two-factor bifactor model with a strong general PTSD factor and two less dominant specific factors – one factor with trauma-related symptoms (re-experiencing and avoidance) and one factor with global psychological symptoms (describing the trauma’s higher-order impact on mood, cognition, behaviour and arousal). From the perspective of clinical utility, we recommend the cut-off scoring method for the German version of the PCL-5. Basic psychometric properties and scale characteristics are provided.

Genomic evidence for the suitability of Göttingen Minipigs with a rare seizure phenotype as a model for human epilepsy.

Epilepsy is a complex genetic disorder that affects about 2% of the global population. Although the frequency and severity of epileptic seizures can be reduced by a range of pharmacological interventions, there are no disease-modifying treatments for epilepsy. The development of new and more effective drugs is hindered by a lack of suitable animal models. Available rodent models may not recapitulate all key aspects of the disease. Spontaneous epileptic convulsions were observed in few Göttingen Minipigs (GMPs), which may provide a valuable alternative animal model for the characterisation of epilepsy-type diseases and for testing new treatments. We have characterised affected GMPs at the genome level and have taken advantage of primary fibroblast cultures to validate the functional impact of fixed genetic variants on the transcriptome level. We found numerous genes connected to calcium metabolism that have not been associated with epilepsy before, such as ADORA2B, CAMK1D, ITPKB, MCOLN2, MYLK, NFATC3, PDGFD, and PHKB. Our results have identified two transcription factor genes, EGR3 and HOXB6, as potential key regulators of CACNA1H, which was previously linked to epilepsy-type disorders in humans. Our findings provide the first set of conclusive results to support the use of affected subsets of GMPs as an alternative and more reliable model system to study human epilepsy. Further neurological and pharmacological validation of the suitability of GMPs as an epilepsy model is therefore warranted.

Inhibition of Insulin-Regulated Aminopeptidase by Imidazo [1,5-α]pyridines-Synthesis and Evaluation.

Inhibition of insulin-regulated aminopeptidase (IRAP) has been shown to improve cognitive functions in several animal models. Recently, we performed a screening campaign of approximately 10,000 compounds, identifying novel small-molecule-based compounds acting as inhibitors of the enzymatic activity of IRAP. Here we report on the chemical synthesis, structure-activity relationships (SAR) and initial characterization of physicochemical properties of a series of 48 imidazo [1,5-α]pyridine-based inhibitors, including delineation of their mode of action as non-competitive inhibitors with a small L-leucine-based IRAP substrate. The best compound displays an IC50 value of 1.0 µM. We elucidate the importance of two chiral sites in these molecules and find they have little impact on the compound's metabolic stability or physicochemical properties. The carbonyl group of a central urea moiety was initially believed to mimic substrate binding to a catalytically important Zn2+ ion in the active site, although the plausibility of this binding hypothesis is challenged by observation of excellent selectivity versus the closely related aminopeptidase N (APN). Taken together with the non-competitive inhibition pattern, we also consider an alternative model of allosteric binding.