Good Model Research Articles

Parenting and other potential protective factors associated with polysubstance use among public school students in Lagos, Nigeria.

Substance use is a growing problem in Nigeria. The present study extended recent work documenting the importance of parenting as protective against substance use in Nigerian youth by testing a model linking parenting, additional protective factors and polysubstance use. Public school students (N = 1607; 56% female; M age = 14.88; SD = .44 years) living in the greater Lagos region participated in school-based data collection. Lifetime polysubstance use, defined as use of two or more substances including alcohol or illicit drugs, or misuse of over-the-counter medications, was reported by 5.2% of the sample. Structural equation modelling that accounted for adolescent age and sex on all constructs revealed good model fit. Positive parenting (support and solicitation) was significantly associated with higher perceived harmfulness of substance use, religiosity and positive relationships at school. Positive school relationships were associated with a decreased likelihood of polysubstance use. Multiple group analysis revealed no overall sex differences in the model paths. Strengthening parent-adolescent relationships may have a cascading effect on protective factors and subsequent substance use, and should be included in youth substance use prevention programmes.

HARMONIC ANALYSISIN INVENTORY MANAGEMENT IN LOGISTICSAND SUPPLY CHAINS

The article examines the possibilities of using harmonic analysis apparatus while planning the need in stocks of various categories. The issues of improving material planning (forecasting) are currently quite relevant applied tasks in business, and are also of interest in terms of developing the theory of logistics and supply chain management. Among the approaches to planning the need for inventory, a wide range of statistical theory methods are used in practice, among which two large groups can be distinguished, such as one-parameter time series analysis models for goods of stable and relatively stable demand, as well as correlation and regression models for chaotic consumption stocks. Most of the goods sold to end consumers have a seasonal component, which may not always be explicitly expressed in statistics. However, due to change of seasons, regular fluctuations in the values of consumption from the warehouse, around the average chronological or trend, may necessitate regular recalculation of the main parameters of inventory management models, such as maximum, insurance, threshold inventory levels, etc. In addition, sales statistics may contain fluctuations of a higher order if the stocks sold have life cycles of interest in goods on the market, which can also be attributed to periodic or quasi-periodic processes. One of the methods to formalize such cyclic processes can be trigonometric approximation or harmonic analysis, when the values of a time series are represented as terms of a Fourier series (harmonics) having corresponding amplitudes and phases. The article compares the effectiveness (accuracy) of forecasts of inventory requirements made using harmonic analysis and traditional time series extrapolation methods.

States of affairs and our connection with the good

AbstractAbstractionists claim that the only bearers of intrinsic value are abstract, necessarily existing states of affairs. I argue that abstractionism cannot succeed. Though we can model concrete goods such as lives, projects, and outcomes with abstract states, conflating models of goods with the goods themselves has surprising and unattractive consequences. I suggest that concrete states of affairs or facts are the only bearers of intrinsic value. I show how this proposal can overcome the concerns lodged against abstractionism and, in the process, reconnect us with the good.

Negative rivalry in use: toward a knowledge-sharing perspective in the digital economy era

Purpose This study aims to introduce and prove the existence of negative rivalry in use, which should be an integral part of goods taxonomy, from the perspective of knowledge sharing and further present the critical role of knowledge sharing in the digital economy era by reviewing the literature, theoretical analysis and real-world cases. It also aims to open a new door for re-recognizing knowledge sharing through an interdisciplinary framework. Design/methodology/approach This study proves the existence of negative rivalry through both theoretical analysis (4-E model) and real-world cases, especially the cases of Tesla and ChatGPT, and puts up new reasons for knowledge sharing in the era of digital economy through interdisciplinary methods. Findings The authors find out that there are many new phenomena beyond the spectrum of current goods taxonomy, especially beyond the priori understanding of rivalry in use. Digital platforms such as ChatGPT should have been “used up” in no time, for they have attracted so massive users according to (positive) rivalry in use, or should have been unchanged at most according to non-rivalry in use. But what we see is their rapid upgrading with the help of billions of users. The reason is that negative rivalry in use has completely been neglected. The authors find out that the process of knowledge sharing unveils the unrecognized attribute of rivalry in use, i.e. negative rivalry in use, which serves as the fundamental driving force of the breathtaking growth of all kinds of digital platforms. Originality/value This study originally put up a 4-E model of goods’ rivalry in use, the brand new term, i.e. negative rivalry in use, and proves its existence and working mechanism from the perspective of knowledge sharing. With the introduction of negative rivalry in use, the traditional four-type goods classification model is extended into a six-type model, which may be a sound marginal contribution, to the best of the authors’ knowledge. The study may reshape people’s mindsets on goods usage, especially knowledge management, into a more open-sharing model because it shows that there is very likely a positive-sum game instead of a zero- or negative-sum game for shared knowledge per se and its platform in the process of knowledge sharing in the era of digital economy.

Foreword for the special issue: “Decision-Making Models for Common Goods in Environmental, Health, and Logistics Contexts”

We present the scientific results of the call for papers in SEPS titled ’Decision-Making Models for Common Goods in Environmental, Health, and Logistics Contexts.’ As guest editors in Socio-economic Planning Sciences (SEPS), we describe the domain of our academic initiative and present the collected papers. Interestingly, our collection includes papers employing various methodologies such as multi-criteria decision making, game theory, operations research, microeconomics, computational models and insurance mathematics. We received more than fifty submissions from Asia, Europe and America. However, only thirteen papers were accepted for publication in SEPS. This demonstrates the extensive investigation of the problem of common goods and the ongoing academic debate surrounding it.

Modified calibration strategies and parameter regionalization potential for streamflow estimation using a hydrological model

ABSTRACT Calibration and regionalization are two key tasks in hydrological modelling, and the availability of increased data and computing resources has enhanced our ability to complete these tasks. This paper addresses both tasks, i.e. (i) it proposes novel model structure-based calibration strategies with the goal to improve estimation of high streamflows, and (ii) it explores the association of soil hydraulic properties with optimal parameters to estimate streamflows in ungauged catchments. Both tasks are demonstrated by employing a conceptual hydrological model, Sacramento Soil Moisture Accounting (SAC-SMA), on multiple catchments in the Narmada River Basin. The initial calibrated model exhibited good model performance, with high flows being underestimated. Seven calibration strategies via revising parameter space are explored, and improvement in high flow performance is observed at nine out of 12 catchments. Estimated streamflows based on soil hydraulic parameters demonstrate regionalization potential. The results indicate successful application of the proposed methods which are transferable to other basins.

Effect of Nursing Intervention Model Guided by BETTER Model on Sexuality Satisfaction and Psychological Status among Chronic Obstructive Pulmonary Disease Women

Effect of Nursing Intervention Model Guided by BETTER Model on Sexuality Satisfaction and Psychological Status among Chronic Obstructive Pulmonary Disease Women

Effects of sexual and gender minority stress on depressive symptoms among adolescents of color in the United States.

There is a need for more research on minority stress theory (MST) with sexual and gender minority (SGM) adolescents of color, because of their disproportionate risk for depression. We recruited 1,627 SGM adolescents of color in the United States to complete measures assessing lesbian, gay, bisexual, transgender, and queer (LGBTQ) climate, LGBTQ microaggressions within one's ethnoracial community, internalized LGBTQ stigma, stress management ability, and depressive symptoms. Using structural equation modeling, a hybrid measurement-structural model was tested, indicating good model fit. Multiple significant indirect pathways linking LGBTQ climate and depressive symptoms emerged. A less positive LGBTQ climate was associated with more microaggression-related stress, more internalized LGBTQ stigma, and worse stress management ability, all of which were associated with greater depressive symptoms. A serial mediation with more microaggression-related stress being associated with greater internalized LGBTQ stigma approached significance. Our findings generally support MST processes in terms of depressive symptoms in SGM adolescents of color, suggesting that psychosocial interventions targeting these processes may have meaningful implications for the mental health of this vulnerable group. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Introspective Deep Metric Learning.

This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods focus on learning a discriminative embedding to describe the semantic features of images, which ignore the existence of uncertainty in each image resulting from noise or semantic ambiguity. Training without awareness of these uncertainties causes the model to overfit the annotated labels during training and produce overconfident judgments during inference. Motivated by this, we argue that a good similarity model should consider the semantic discrepancies with awareness of the uncertainty to better deal with ambiguous images for more robust training. To achieve this, we propose to represent an image using not only a semantic embedding but also an accompanying uncertainty embedding, which describes the semantic characteristics and ambiguity of an image, respectively. We further propose an introspective similarity metric to make similarity judgments between images considering both their semantic differences and ambiguities. The gradient analysis of the proposed metric shows that it enables the model to learn at an adaptive and slower pace to deal with the uncertainty during training. Our framework attains state-of-the-art performance on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets for image retrieval. We further evaluate our framework for image classification on the ImageNet-1 K, CIFAR-10, and CIFAR-100 datasets, which shows that equipping existing data mixing methods with the proposed introspective metric consistently achieves better results (e.g., +0.44% for CutMix on ImageNet-1 K).

Adverse childhood event subtypes and routine diabetes care utilization among U.S. adults with Type 2 diabetes.

Adverse childhood events (ACEs) have significant impacts on adulthood health, including greater risk of Type 2 diabetes (T2D). While there is a known connection to increased use of outpatient and emergency healthcare services, the potential role of ACEs in routine diabetes care utilization remains unclear. This study uses ACE subtypes to explain pathways to routine diabetes care utilization among adults with T2D. Cross-sectional data were obtained from the 2019 Behavioral Risk Factor Surveillance System, a survey of a representative sample of U.S. adults. Eligible participants resided in a state that completed both the Adverse Childhood Experiences (ACEs) and Diabetes modules and were diagnosed with non-gestational diabetes (N = 9,904), of whom the majority were presumed to have T2D. Confirmatory factor analysis was used to test the measurement model for three ACE factors: household dysfunction, physical and emotional abuse, and sexual abuse. Structural equation modeling was used to relate factors to four routine diabetes care utilization outcomes. Factor loadings were strong, and fit indices indicated good measurement model and full structural model fits. In the full structural equation model, household dysfunction was associated with decreased likelihood of meeting frequency recommendations for A1C testing (β = -0.22, p < .05) and foot exams (β = -0.29, p < .01). Physical and emotional abuse were associated with greater likelihood of meeting A1C testing frequency recommendations (β = 0.21, p < .05). Specific types of ACEs may differentially relate to routine diabetes care utilization in adulthood. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Modelling and estimating trajectory points from RTK-GNSS based on an integrated modelling approach

The sparse Gaussian process regression (GPR) has been used to model trajectory data from Real time kinematics-global navigation satellite system (RTK-GNSS). However, upon scrutinizing the model residuals; the sparse GPR model poorly fits the data and exhibits presence of correlated noise. This work attempts to address these issues by proposing an integrated modeling approach called GPR-LR-ARIMA where the sparse GPR was integrated with the linear regression with autoregressive integrated moving average errors (LR-ARIMA) to further enhance the description of the trajectory data. In this integrated approach, the predicted trajectory points from the GPR were further described by the LR-ARIMA. Simulation of the GPR-LR-ARIMA on three sets of trajectory data indicated better model fit, revealed in the normally distributed model residuals and symmetrically distributed scatter plots. Correlated noise was also successfully eliminated by the model. The GPR-LR-ARIMA outperformed both the GPR and LRARIMA by its ability to improve mean-absolute-error in 2-dimension positioning by up to 86%. The GPR-LR-ARIMA contributes to enhancement of positioning accuracy of dynamic GNSS measurements in localization and navigation system with good model fit.

Exploring science identity among Jordanian high school students: a case study

This study aimed to investigate the validity and reliability of the Arabic version of the science identity scale (SIS) and to explore any differences in levels of science identity due to gender. The study sample comprised 304 male and female high school students who had completed the SIS. To achieve study objectives, exploratory factor analysis and confirmatory factor analysis were used. The results of the exploratory factor analysis showed that four factors explained (40.542%) of total variance. The results of the confirmatory factor analysis indicated a good model fit, and the Cronbach alpha was calculated to be 0.85 for the SIS. Finally, the results revealed statistically significant differences in the level of science identity and its subscales (science performance, science competence, science recognition, and science interest) based on the gender variable, with females scoring higher. The study recommends that teachers utilize the SIS to assess the level of science identity among students and implement teaching practices aimed at enhancing the science identity of secondary school students.

Martial races as clubs? The institutional logic of the martial race system of British India

Military institutions can be seen as a solution to a type of principal-agent problem, in which a government principal contracts with a military agent to produce violence on their behalf. Absent extensive monitoring, low effort should be expected from the military agent. This should especially be the case for a mercenary force which cannot rely on patriotism or ideology. Yet the mercenary army of the British-ruled India consistently produced high and dedicated performance. In this paper, I argue that the otherwise curious institution of recruiting predominantly from the so-called ‘martial races’ helps to explain this puzzle. Drawing on Iannaccone’s club goods model, I argue that the martial race recruitment system represented an effective solution to the principal-agent problem by allowing the British to benefit from a repeat business arrangement with a small number of ethno-religious groups whose costly in-group prohibitions and poor outside options helped both to screen out potential shirkers and deter poor performance on the battlefield.

Development and validation of the gratitude for food scale for Japanese adults.

Gratitude for food is a concept associated with various aspects of healthy and sustainable diet and originates from Buddhism and Shintoism in Japan. Against this background, a scale specifically targeting adults is essential for boosting studies concerning gratitude for food. This study aimed to adapt the Gratitude for Food Scale (GFS), originally developed for Japanese children, to Japanese adults and to examine the reliability and validity of GFS for adults (GFS-A). This cross-sectional study used baseline and one-week follow-up data from a longitudinal survey. The analysis included 1800 Japanese adults at baseline and 1380 at the one-week follow-up based on completion of self-administered questionnaires in February 2023. Construct validity was confirmed using exploratory and confirmatory factor analyses. Further, Spearman's and intraclass correlation coefficients and Cronbach's alpha were calculated to confirm criterion validity and reliability (test-retest and internal consistency). Participants' mean (SD) age was 40.2 (11.2) years (women: n = 900, 50.0%) at baseline. The analysis revealed a one-factor structure comprising five items with good model fit. Criterion validity was confirmed by the correlation coefficients between the total GFS score and the gratitude questionnaire and the sub-score of awareness and appreciation for food, which represents a subscale of the expanded mindful eating scale. Moreover, test-retest reliability and internal consistency were confirmed. From this study's results, we can see that the developed GFS-A is an appropriate scale with good reliability and validity for measuring Japanese adults' gratitude for food.

Toward Atomistic Understanding of Iron Phosphate Glass: A First-Principles-Based Density Functional Theory Modeling and Study of Its Physical Properties.

Iron phosphate glasses (IPGs) have been proposed as futuristic materials for nuclear waste immobilization and anode materials for lithium batteries. Recently, many attempts have been made to propose atomistic models of IPGs to explain their properties from an atomistic viewpoint. In this paper, we seek to produce small scale models of IPG that can be handled within the scheme of density functional theory (DFT) to study its electronic structure. The starting models, generated using the Monte Carlo (MC) method, were subsequently annealed using ab initio molecular dynamics (AIMD) to minimize the coordination defects. The geometry of the equilibrated structure was then optimized at 0 K. This hybrid approach (MC + AIMD + DFT optimization) produced good atomistic models of IPG, which can reproduce the experimentally observed band gap, vibrational density of states, magnetic moment of Fe, and mechanical and optical properties. Computationally expensive melt-quench simulation can be avoided using the present approach, allowing the use of DFT for accurate calculations of properties.

Psychometric Validation of the Persian Version of Multidimensional Health Locus of Control Scale (MHLC-C) for Menopausal Women

Background. The Health Locus of Control (HLC) serves as a mediator between an individual’s behaviors and health status, influenced by various cultural, environmental, and situational factors. Its measurement reflects health beliefs and attitudes, ultimately leading to healthy behaviors. Menopause is a significant phenomenon in women’s lives, often resulting in a wide range of symptoms and health issues. Therefore, the perception of HLC plays a crucial role in promoting healthy behaviors and managing menopausal symptoms. This study aimed to assess the validity and reliability of the Persian version of the short form Multidimensional Health Locus of Control Scale (MHLC) in Iranian menopausal women and explore its interrelationships. Methods. A cross-sectional study was conducted with 271 menopausal women from September to November 2023. Psychometric properties such as construct validity (confirmed through confirmatory factor analysis), convergent validity (evaluated using average variance extracted), and internal consistency (assessed by Cronbach’s alpha, composite reliability and rho_A factor) were examined. Discriminant validity was determined using the Fornell–Larcker criterion. Reliability was further established through Cronbach’s alpha and McDonald’s Omega coefficients. The predictive relevance of the model and associations between constructs were analyzed using various statistical measures. Data analysis was conducted using SPSS v.27 and Smart PLS 3.0 software. Limitations of this study include that it was conducted in urban health centers, only women with at least primary education were included, and reliance on self-reported data. Results. The initial stage of construct validity involved the Kolmogorov–Smirnov test, with confirmatory factor analysis demonstrating a good model fit. Significant correlations were found between internal health control and factors related to doctors and other people, indicating that internal health control directly influences these factors in managing menopausal symptoms and health problems. Cronbach’s alpha and McDonald’s Omega coefficients for the 13-item MHLC-C scale were satisfactory (0.81 and 0.72, respectively). Conclusions. The Persian version of the 13-item MHLC-C has been validated as a reliable tool for assessing the health locus of control in Iranian menopausal women. Internal health control was found to significantly impact perceptions related to doctors and other people, highlighting its importance in managing menopausal symptoms effectively.

Reflections on statistical leadership: Summary of a panel discussion at the WSC 20231

What qualities are needed by statisticians to achieve top leadership positions in academia, business, industry and government? Five leaders from statistical societies, national and international statistical offices, and academia share their experiences. They respond to five specific questions. Firstly, is leadership just needed by top management or do all statisticians have a role? If so, what is it? Secondly, do statisticians naturally make good leaders? What new skills do they need to acquire? What skills advantages do they have? Thirdly, the panel consider the question: did you work for people who were not good leaders? How did they fall short? What good role models did panellists have? And then, is it harder for women, and for other under-represented groups? And finally they were asked: what message do you have for young statisticians aspiring to leadership roles?

Evaluating the Robustness of a Deep Learning Bone Age Algorithm to Clinical Image Variation Using Computational Stress Testing.

Purpose To evaluate the robustness of an award-winning bone age deep learning (DL) model to extensive variations in image appearance. Materials and Methods In December 2021, the DL bone age model that won the 2017 RSNA Pediatric Bone Age Challenge was retrospectively evaluated using the RSNA validation set (1425 pediatric hand radiographs; internal test set in this study) and the Digital Hand Atlas (DHA) (1202 pediatric hand radiographs; external test set). Each test image underwent seven types of transformations (rotations, flips, brightness, contrast, inversion, laterality marker, and resolution) to represent a range of image appearances, many of which simulate real-world variations. Computational "stress tests" were performed by comparing the model's predictions on baseline and transformed images. Mean absolute differences (MADs) of predicted bone ages compared with radiologist-determined ground truth on baseline versus transformed images were compared using Wilcoxon signed rank tests. The proportion of clinically significant errors (CSEs) was compared using McNemar tests. Results There was no evidence of a difference in MAD of the model on the two baseline test sets (RSNA = 6.8 months, DHA = 6.9 months; P = .05), indicating good model generalization to external data. Except for the RSNA dataset images with an appended radiologic laterality marker (P = .86), there were significant differences in MAD for both the DHA and RSNA datasets among other transformation groups (rotations, flips, brightness, contrast, inversion, and resolution). There were significant differences in proportion of CSEs for 57% of the image transformations (19 of 33) performed on the DHA dataset. Conclusion Although an award-winning pediatric bone age DL model generalized well to curated external images, it had inconsistent predictions on images that had undergone simple transformations reflective of several real-world variations in image appearance. Keywords: Pediatrics, Hand, Convolutional Neural Network, Radiography Supplemental material is available for this article. © RSNA, 2024 See also commentary by Faghani and Erickson in this issue.

Multispecies comparative prostate anatomy by imaging: Implications for experimental models of prostatic disease.

There is an increasing interest in using preclinical models for development and assessment of medical devices and imaging techniques for prostatic disease care. Still, a comprehensive assessment of the prostate's radiological anatomy in primary preclinical models such as dogs, rabbits, and mice utilizing human anatomy as a reference point remains necessary with no optimal model for each purpose being clearly defined in the literature. Therefore, this study compares the anatomical characteristics of different animal models to the human prostatic gland from the imaging perspective. We imaged five Beagle laboratory dogs, five New Zealand White rabbits, and five mice, all sexually mature males, under Institutional Animal Care and Use Committee (IACUC) approval. Ultrasonography (US) was performed using the Vevo® F2 for mice (57 MHz probe). Rabbits and dogs were imaged using the Siemens® Acuson S3000 (17 MHz probe) and endocavitary (8 MHz) probes, respectively. Magnetic resonance imaging (MRI) was also conducted with a 7T scanner in mice and 3T scanner in rabbits and dogs. Canine transrectal US emerged as the optimal method for US imaging, depicting a morphologically similar gland to humans but lacking echoic zonal differentiation. MRI findings in canines indicated a homogeneously structured gland similar to the human peripheral zone on T2-weighted images (T2W) and apparent diffusion coefficient (ADC). In rabbits, US imaging faced challenges due to the pubic symphysis, whereas MRI effectively visualized all structures with the prostate presenting a similar aspect to the human peripheral gland on T2W and ADC maps. Murine prostate assessment revealed poor visualization of the prostate glands in ultrasound due to its small size, while 7T MRI delineated the distinct prostates and its lobes, with the lateral and dorsal prostate resembling the peripheral zone and the anterior prostate the central zone of the human gland. Dogs stand out as superior models for advanced preclinical studies in prostatic disease research. However, mice present as a good model for early stage studies and rabbits are a cost-effective alternative and serve as valuable tools in specific research domains when canine research is not feasible.

Development of a genetic system for Haloferax gibbonsii LR2-5, model host for haloarchaeal viruses.

Species from all domains of life are infected by viruses. In some environments, viruses outnumber their microbial hosts by a factor of 10, and viruses are the most important predators of microorganisms. While much has been discovered about the infection mechanisms of bacterial and eukaryotic viruses, archaeal viruses remain understudied. Good model systems are needed to study their virus-host interactions in detail. The salt-loving archaeon Haloferax gibbonsii LR2-5 has been shown to be infected by a variety of different viruses and, thus, is an excellent model to study archaeal viruses. By establishing a genetic system, we have significantly expanded the toolbox for this model organism, which will fuel our understanding of infection strategies of the underexplored archaeal viruses.