Model Principle Research Articles

Building a pelvic organ prolapse diagnostic model using vision transformer on multi-sequence MRI.

Although the uterus, bladder, and rectum are distinct organs, their muscular fasciae are often interconnected. Clinical experience suggests that they may share common risk factors and associations. When one organ experiences prolapse, it can potentially affect the neighboring organs. However, the current assessment of disease severity still relies on manual measurements, which can yield varying results depending on the physician, thereby leading to diagnostic inaccuracies. This study aims to develop a multilabel grading model based on deep learning to classify the degree of prolapse of three organs in the female pelvis using stress magnetic resonance imaging (MRI) and provide interpretable result analysis. We utilized sagittal MRI sequences taken at rest and during maximum Valsalva maneuver from 662 subjects. The training set included 464 subjects, the validation set included 98 subjects, and the test set included 100 subjects (training set n=464, validation set n=98, test set n=100). We designed a feature extraction module specifically for pelvic floor MRI using the vision transformer architecture and employed label masking training strategy and pre-training methods to enhance model convergence. The grading results were evaluated using Precision, Kappa, Recall, and Area Under the Curve (AUC). To validate the effectiveness of the model, the designed model was compared with classic grading methods. Finally, we provided interpretability charts illustrating the model's operational principles on the grading task. In terms of POP grading detection, the model achieved an average Precision, Kappa coefficient, Recall, and AUC of 0.86, 0.77, 0.76, and 0.86, respectively. Compared to existing studies, our model achieved the highest performance metrics. The average time taken to diagnose a patient was 0.38s. The proposed model achieved detection accuracy that is comparable to or even exceeds that of physicians, demonstrating the effectiveness of the vision transformer architecture and label masking training strategy for assisting in the grading of POP under static and maximum Valsalva conditions. This offers a promising option for computer-aided diagnosis and treatment planning of POP.

Performance of a Protein Language Model for Variant Annotation in Cardiac Disease.

Genetic testing is a cornerstone in the assessment of many cardiac diseases. However, variants are frequently classified as variants of unknown significance, limiting the utility of testing. Recently, the DeepMind group (Google) developed AlphaMissense, a unique artificial intelligence-based model, based on language model principles, for the prediction of missense variant pathogenicity. We aimed to report on the performance of AlphaMissense, accessed by VarCardio, an open web-based variant annotation engine, in a real-world cardiovascular genetics center. All genetic variants from an inherited arrhythmia program were examined using AlphaMissense via VarCard.io and compared with the ClinVar variant classification system, as well as another variant classification platform (Franklin by Genoox). The mutation reclassification rate and genotype-phenotype concordance were examined for all variants in the study. We included 266 patients with heritable cardiac diseases, harboring 339 missense variants. Of those, 230 (67.8%) were classified by ClinVar as either variants of unknown significance or nonclassified. Using VarCard.io, 198 variants of unknown significance (86.1%, 95% CI, 80.9-90.3) were reclassified to either likely pathogenic or likely benign. The reclassification rate was significantly higher for VarCard.io than for Franklin (86.1% versus 34.8%, P<0.001). Genotype-phenotype concordance was highly aligned using VarCard.io predictions, at 95.9% (95% CI, 92.8-97.9) concordance rate. For 109 variants classified as pathogenic, likely pathogenic, benign, or likely benign by ClinVar, concordance with VarCard.io was high (90.5%). AlphaMissense, accessed via VarCard.io, may be a highly efficient tool for cardiac genetic variant interpretation. The engine's notable performance in assessing variants that are classified as variants of unknown significance in ClinVar demonstrates its potential to enhance cardiac genetic testing.

Application of Simulation Modeling and Lean Principles for Reducing Patient Waiting Queues and Cost: The Case Study of a Developing Country

Application of Simulation Modeling and Lean Principles for Reducing Patient Waiting Queues and Cost: The Case Study of a Developing Country

PRINCIPLES OF SIMULATION OF ENERGY-EFFICIENT GRANULATION PROCESSES IN A FLUIDIZED BED

The principles of physical and mathematical modeling of inhomogeneous jet-pulsating fluidization in the self-oscillating mode considering the movement of solid granulated particles on the working surfaces of the gas distributing device are investigated. The research tasks include substantiating the method of interaction between the gas phase and granular solids to ensure the implementation of heterogeneous jet-pulsating fluidization in the self-oscillating mode, experimentally determining hydrodynamic parameters to minimize the risk of stagnant zones on the working surface of the gas distributing device and formulating principles for introducing flat gas jets into orthogonal planes through a gas distribution device. The physical model focuses on creating heterogeneous jet-pulsating fluidization which is based on the formation of heterogeneous porosity in the bed of solids within the apparatus chamber. The study highlights the importance of rationally organizing the interaction mode between the gas phase and solids to ensure active circulation of granular material between 3 main technological zones: humidification, active heat exchange and relaxation. This organization enables sufficient mass transfer along the height of the bed and getting a product with the desired properties. Implementing the proposed modeling principles in equipment development will allow increasing the heat utilization coefficient by more than 60 % and will facilitate the implementation of innovative technology. The research contributes to the development of fluidization technology and its application in granulation at temperatures exceeding the melting point of thermolabile components, ensuring efficient use of resources and energy, and enhancing the environmental safety of technological processes. Bibl. 22, Fig. 11.

Critical Review of Physical-Mechanical Principles in Geostructure-Soil Interface Mechanics

AbstractDue to the relatively different mechanical and physical properties of soils and structures, the interface plays a critical role in the transfer of stress and strain between them. The stability and safety of geotechnical structures are thus greatly influenced by the behavior at the soil–structure interface. It is therefore important to focus on the unique characteristics that set the interface apart from other geomaterials while examining the interface behaviour. Understanding the physical mechanism and modelling principles of these interfaces becomes a crucial step for the secure design and investigation of soil-structure interaction (SSI) issues. Moreover, to deal with this soil-environment interaction problem, the classical soil mechanics formulation must be progressively generalised in order to incorporate the effects of new phenomena and new variables on SSI behaviour. Considering the variety of energy geostructures that are emerging nowadays, it is crucial to comprehend the thermo-hydro-mechanical (THM) behaviour of the interface. The objective of this study is to fill this information gap as concisely as possible. A critical review is provided along with the state-of-the-art information on the thermo-hydro-mechanical behaviour of the soil-structure interface, including testing tools and measurement methods, basic principles and deformation mechanisms, constitutive models, as well as their applications in numerical simulations. This study explains how loading influences the mechanisms at the interface and critically examines the effects of boundary conditions, soil properties, environmental factors, and structure type on the THM behaviour of interface zones between soils and structural elements. The validity and reliability of the interface shear stress-displacement models are also covered in this paper. Lastly, the trends and recent advancements are also recommended for the interface research.

Dynamic uncertainty evaluation of cylindricity error based on Bayesian deep neural network propagation method

Most of the existing methods for measuring and evaluating product geometric tolerances are for static processes, which use lower dimensions, are more complex to calculate, and have poor traceability and dynamic manageability and control of the measurement process. In this paper, a dynamic evaluation model of cylindricity error uncertainty based on the Bayesian deep neural network propagation method is proposed. Firstly, the input random variable sampling sample containing error uncertainty is generated by proposing the improved Monte Carlo sampling method (IMCS), which has the characteristics of homogeneity and validity; then the principle and construction process of a Bayesian neural network model are introduced to generate the propagation model of multidimensional random error variables; further, the Gaussian mixture model is trained by the Monte Carlo method, and the probability density function of the target response is generated by using the Gaussian mixture model. Through numerical experiments, test analysis of bearing outer rings, and comparison with ISO standard methods, the results show that the model based on the dynamic Bayesian deep neural network propagation method can correctly and effectively assess the uncertainty dynamically, and the whole estimation process is stable.

Coupled Modeling and Design Principles of Limited-Angle Vibration Motors for High-Frequency Reciprocating Rotation

Coupled Modeling and Design Principles of Limited-Angle Vibration Motors for High-Frequency Reciprocating Rotation

Stress-strain state of dental immediate prosthesis and associated tissue under the influence of external force

Purpose: to analyze the stress-strain state (SSS) of a removable plate immediate denture with a printed dentition made of polymethyl methacrylate (PMMA) and a polyethylene terephthalate (PET) base under multifactor loading conditions based on the principles of mathematical modeling. Materials and methods: For mathematical modeling of SSS, Comsol Multiphysics 5.6 software (Comsol Inc., USA) was used, which provided numerical data for analytical interpretation. We considered a flat 2D model consisting of a printed monolithic dentition and a PMMA bonding layer, a PET base, the mucous membrane of the prosthetic field and jaw bone tissue. Results: The fields of von Mises stresses (VM) and deformations under the influence of normal, oblique and tangential loads (up to 500 N), the localization of their maximum values and the average value inside the prosthetic structure, mucous membrane and bone tissue were determined. The maximum values of VM in the mucous membrane are compared with the pain threshold based on known literature data on algesiometry of the oral mucosa. An assessment was made of the critical length of microcracks (CLM) according to Griffiths, which lead to the formation of a main crack followed by brittle fracture of the structure base. Using the phase field method, the probability of crack formation in the considered model of an orthopedic structure under the influence of static loads of various magnitudes and directions was calculated. Conclusions: Using the method of mathematical modeling of SSS, it was shown that failures when using immediate dentures with printed teeth made of PMMA and a PET base can be caused by the effect of overloading the areas of the base and caused by the deformation process inside the prosthetic structure. The maximum stress in the prosthetic elements was 79.7 MPa and deformation – 0.47 mm. The pain threshold of the oral mucosa is higher than the highest stress value in the prosthetic bed according to Mises (0.002 MPa) for normal load of 500 N. The SSS heterogeneity coefficient of the removable plate immediate prosthesis model is 8.6. The critical length of a microcrack according to Griffiths is lcrit = 1.9...2.7 µm. Using the Comsol Multiphysics phase field method, it was established that the probability of the formation of a large crack in the base of the prosthesis was 6·10–3 %, 0.36 %,0.73 % for a normal, inclined and shear load of 500 N, which is a sufficient value for the operation of removable laminar dentures with printed teeth from PMMA and a PET base during the warranty period.

초등학교 체육수업에서 인공지능 자세추정 앱을 적용한 교육적 효과 탐색 - 네트형 경쟁 활동 배구 언더핸드 동작을 중심으로

Purpose: The purpose of this study is to explore the educational effects and assess the improvement of movements by applying an AI-based app that estimates posture and provides feedback on underhand volleyball skills to elementary school students. Methods: The research participants consisted of 177 students (age: 12 years old, height: 152.8±7.01 cm, weight: 46.72±11.2 kg, BMI: 19.90±3.68 kg/㎡) attending Elementary School located in A City. First, the volleyball underhand pass motion was analyzed to confirm the order and method for teaching the correct posture, and based on this, feedback to be used in the app was constructed. Next, through previous research on the types and operating principles of artificial intelligence posture estimation models Learned how to create an app to run on a web page. Based on this, a plan was established and an app to be used in class was developed directly. Results: The results that the experimental group showed an increase of approximately 7 minutes in practice time, whereas the comparison group showed a decrease of about 6 minutes (F = 20.873, p &lt; .001). In terms of students' perceptions and degree of change after the app-based class, the observability domain increased after the app-based class (F = 49.216, p &lt; .001). The testability domain also increased after the app-based class (F = 71.017, p &lt; .001). Conclusion: Through the use of an AI posture estimation app in net-type competitive activities, students were able to perform the correct underhand volleyball technique, and their learning effects during the lessons also improved.

Turkish Adaptation, Validity and Reliability Study of The MIND Diet Scale for Delaying Neurodegeneration

Background: MIND diet is a nutritional model that has positive effects on neurological diseases, cognitive function and mental health, as it contains nutrients with antioxidant properties. The MIND diet scale was developed to evaluate the diet's adherence with the MIND nutritional model principles. Objectives: This study aimed to adapt the MIND Diet Scale into Turkish and assess validity, reliability of the scale in the Turkish population. Method: Language adaptation of the scale was provided and the serving sizes in the scale items were adapted to our country. Then, the scale was applied to volunteer participants aged 18 and over. The data of 150 participants were analyzed. Content, concurrent validity and reliability of the scale was tested. Cronbach’s α (internal consistency) and test-retest reliability were used to assess the reliability. Results: The Cronbach's Alpha value of the 15-item MIND diet scale was 0.626 and the scale was found to have moderate reliability. Test and retest correlation also shows that the MIND diet scale is a reliable scale (r=0.591; p

Enhancing the Efficiency of Scientific Activities in a Medical Organization within the Scope of the Process Approach

The article substantiates the principal trends of research optimization in medical organizations with the use of process-oriented management methods (the case of Ya.L.Tsivyan Novosibirsk Research Institute of Traumatology and Orthopedics). The research is based on the principle of the process approach. It is one of the fundamental principles of the total quality management model that is the most effective management concept based on quality. When developing a system for organizing scientific workflows based on the process approach, planning as well as a methodology of academic research play the key role. For all stages of research activities, processes of planning tasks, their fulfilment and reporting with the determination of responsibility for all types of work being performed and with the development of forms of necessary documents used in the organization of scholarly activities have been framed; approaches to the output of scientific products have been standardized and process regulations have been developed for each of its types. The development of processes is carried out on the basis of the requirements of regulatory documentation, taking into account modern approaches to the organization of research activities. Moreover, all documents containing requirements for processes are reviewed and updated at regular intervals, taking into account changes in regulatory guidance. The development and implementation of a system for organizing scientific activities based on the process approach allow a scientific medical institution to achieve compliance with modern requirements for conducting academic research, as well as to improve the quality and efficiency of scientific activities. The accumulated successful experience can be used in organizations that seek to increase the effectiveness of their scientific activities and meet contemporary requirements for their results.

Retrodiction of forest demography: Backward simulation with reverse matrix models

Abstract Reconstructing past ecological population dynamics and demographic events is crucial for understanding the dynamics of ecological processes, evaluating the impact of environmental changes and making informed conservation decisions. In forest ecosystems, retrodiction (i.e. the backward projection of ecological populations) plays a pivotal role in understanding historical forest carbon levels and the factors that have influenced their variation over time, because forest demography is a major determinant of the amount of carbon stored in forest ecosystems. The persistent lack of quantitative methods has been a significant obstacle in retrodicting forest demography, especially in applications of a broad geographical scale. While there is a wealth of models for predicting future forest conditions, models that can project these conditions backward in time are scarce. This study presents reverse matrix model (RMM), an innovative retrodiction modelling approach grounded in the principles of transition matrix models. RMM is designed to deduce past demographic characteristics of ecological populations using current data, making it one of the first models capable of projecting the fine‐scale dynamics of forest demography into the past. We assessed the retrodictive performance of RMM by fitting it to a dataset of a disturbed tropical rainforest in French Guiana in 2001–2023, then comparing the retrodictions to observations back to 1983 when the disturbance occurred. We further empirically evaluated the viability of retrodiction over a defined duration by inverting the density‐dependent matrix model by Lin et al. (1996), which predicts the dynamics of northern hardwoods in the United States. The case studies demonstrate significant potential for RMM application in various domains of forestry and conservation, including ecosystem management and conservation planning, global change impact assessment and biodiversity monitoring.

Design Thinking for Frugal Innovation: Unleashing Sustainable Business Models in Emerging Markets

Frugal innovation is a hotbed for low-cost innovative solutions that addresses the needs of underprivileged customers in emerging markets. Many of such frugal innovations are designed and developed by organizations that work at the grassroots, and, develop solutions in response to the interests of disadvantaged communities. With a central tenet of transforming an existing or undesirable state into a desired state, Design plays a significant role in meeting the unmet needs of such disadvantaged communities. Lately, Design Thinking is increasingly gaining traction as a human-centered approach to innovation that focuses on creative, innovative, and iterative development. The foundation of Design Thinking lies in a strong desire to understand more about the people for whom the products and services are developed. When combined with the principles of sustainable business models, design thinking can lead to frugal innovations that not only meet the needs of underprivileged customers but also engender long-term social, economic, and environmental implications. However, studies focusing on the interplay of design thinking and sustainable business models, to unravel the outcome of frugal innovation are scanty. The current study fills this knowledge gap by uncovering how design thinking and sustainable business models can jointly help in improving the outcomes of frugal innovation practices in emerging markets. The research methodology comprises qualitative data collection through a multi-case study strategy using a grounded theory approach. The findings result in a conceptual framework that can be utilized to assess the performance metrics of organizations practicing frugal innovation in an emerging market context. The framework suggests that the outcome of frugal innovation can be measured using various indicators such as affordability, accessibility, energy efficiency, empowerment, and a human-centric approach. By focusing on human-centered solutions and maximizing the potential of frugal innovation, the study’s contribution can potentially offer insights for organizations aiming to create a sustainable impact.

Integrating equity and justice principles in water resources modeling and management

Integrating equity and justice principles in water resources modeling and management

Energy management optimization of hybrid electric vehicles based on deep learning model predictive control

In this paper, the hybrid electric vehicle (HEV) energy management optimization method is proposed based on deep learning (DL) model predictive control. Through empirical research combined with the questionnaire survey, this article not only provides a new perspective and practical basis but also improves the efficiency and accuracy of the model by improving the relevant algorithms. The study first analyzes the importance of HEV energy management and reviews the existing literature. Then, the optimization method of HEV energy management based on the deep learning model is introduced in detail, including the composition of energy management for hybrid electric vehicles, the structure and working principle of the deep learning model, especially the backpropagation neural network (BPNN) and the convolutional neural network (CNN), and the steps of application of deep learning in energy management. In the experimental part, questionnaire data from 1,500 consumers were used to design the HEV energy management optimization scheme, and consumers’ attitudes and preferences towards HEV energy optimization were discussed. The experimental results show that the proposed model can predict HEV energy consumption under different road conditions (urban roads, highways, mountain areas, suburban areas, and construction sites), and the difference between the average predicted energy consumption and the actual energy consumption is between 0.1KWH and 0.3KWH, showing high prediction accuracy. In addition, the deep learning-based energy management strategy outperforms traditional control strategies in terms of fuel consumption (6.2 L/100 km), battery charge and discharge times (814), battery life, and CO2 emissions, significantly improving the efficiency of HEV energy. These results demonstrate the great potential and practical application value of deep learning models in the optimization of energy management of HEVs, helping to drive the development of more sustainable and efficient transportation systems.

Teacher effectiveness in English-medium instruction and students’ academic achievement: A value-added model in action

This study aimed to investigate the association between general science teachers’ effectiveness in teaching through English and students’ academic success in Debre Birhan City. In addition, it sought to determine which dimensions of English as a medium of instruction (EMI) accounted for the greatest variability. Furthermore, it examined whether demographic variables such as sex, qualification, and experience co-variated with students’ academic achievement (SAA). A non-experimental correlational design was employed. The study sample included 45 randomly selected teachers and 1575 students in 45 classrooms. A teacher effectiveness questionnaire and an academic achievement test were used as data collection tools implementing the principle of the value-added model. The findings revealed a strong relationship between teachers’ effectiveness in teaching science through English and SAA, with pedagogical knowledge being the highest contributor and experience co-variating with SAA. The unstandardized coefficient output also reveals that SAA increases by a certain percentage as every scale of EMI increases. The study recommends on-the-job training for EMI teachers. Additionally, experienced teachers should work in pairs with younger teachers and share fresh perspectives on teaching methods and effective language use. The introduction of EMI implementation policies is also recommended.

Modeling the Toxicokinetics of Suspensions of Soluble Metallic Nanomaterials.

Proper risk assessment of the many new nanoforms (NFs) that are currently being developed and marketed is hindered by constraints in time and resources for testing their fate and (eco) toxicity profile. This problem has also been encountered in conventional chemical risk assessments, where the definition of related chemical groups can facilitate risk assessment for all class members. Whereas grouping and read-across methods are well established, such approaches are in the early stages of development for NFs. In this study, a modeling framework was developed for grouping NFs into distinct classes regarding the contribution of released ions to suspension-induced toxicity. The framework is based on combining dissolution rate constants of NFs with information about the toxicokinetics of the NFs and the dissolution products formed. The framework is exemplified for the specific case of suspension toxicity of metallic NFs (silver and copper). To this end, principles of mixture toxicity and dose-response modeling are integrated to derive threshold values for the key NF properties determining suspension toxicity: size, shape, and chemical composition. The threshold values thus derived offer a possible solution for the high-throughput screening of NFs according to their morphological and compositional properties in a regulatory context.

Digital Active Learning Strategies in Blended Environments to Develop Students' Social and Emotional Learning Skills and Engagement in Higher Education

ABSTRACTThe COVID‐19 pandemic has led to a shift towards online and blended learning; however, most students and faculty members still report more challenges in online courses regarding social interaction and engagement. This paper aimed to design an interactive blended course following Merrills' first principles of instruction model to develop students' social and emotional learning skills and engagement in higher education. A mixed‐method design, including questionnaires and interviews, was used with a sample population which consisted of 90 graduate students and divided into two groups, an experimental and a control group. The results showed that there were statistically significant differences between the experimental and control groups in‐terms of social–emotional learning skills and students' engagement due to the high perception of the experimental group. The findings also revealed that the most dominant social–emotional learning skills that appeared to be supported by applying these strategies from the students' point of view were self‐awareness and social awareness. The study also found that instructor presence, effective course design and appropriate use of technology significantly influenced students' engagement.

Implementing Patient-Centered Outcomes Research Institute Stakeholder Engagement Principles in Models of Palliative Care Delivery and Advance Care Planning Research.

Given the many challenges of conducting research that addresses the palliative and end-of-life care needs of patients with serious illnesses, stakeholder engagement starting from the moment of study conceptualization and design is critical to ensure successful participant recruitment, data collection, intervention delivery, data analysis, and dissemination. Guided by a conceptual model published by the Patient-Centered Outcomes Research Institute (PCORI) entitled, "Measuring What Matters for Advancing the Science and Practice of Engagement"14 and with the support of a PCORI Engagement Officer, representatives from 9 PCORI-funded study teams formed a working group to survey team members and review, outline, and describe key lessons learned and best practices for promoting stakeholder engagement in palliative care research. Almost all study teams engaged with patients/caregivers, clinicians, researchers, and health care system experts as stakeholder partners. About half the teams also included payers and training institutions as part of their stakeholder advisors as well as a range of content experts. Study teams relied on a variety of support structures and resources, and they employed 10 distinct methods for maintaining engagement. All engagement methods were generally considered to be effective by teams who used the method, though there was some variability in team-rated engagement quality of each method. Nine barriers to stakeholder engagement were identified across the 9 studies as well as 9 strategies (or facilitators) to overcome these barriers. We share examples of how stakeholder engagement impacted studies in all phases, including the preparatory phase, study initiation phase, execution phase, and data analysis/dissemination phase. Teams utilized a variety of resources and support structures as well as capitalized on multiple engagement methods for fostering stakeholder engagement, resulting in a high level of collaboration and integration.

Multiobjective Optimization Strategy for Enhancing the Efficiency and Quality of Organic Thin-Film Manufacturing with Electrohydrodynamic Atomization Coating.

Electrohydrodynamic atomization coating technology is well-suited for micro-/nanoscale thin-film additive manufacturing. However, there are still some challenges in quality control and parameter adjustment during the coating process. Especially when coating on nonconductive and nonhydrophilic substrates, film quality and thickness uniformity are difficult to control. This paper proposes an optimization strategy for enhancing the efficiency and quality of thin-film manufacturing on nonconductive, nonhydrophilic glass substrates. In this paper, a visual inspection system was developed for in situ inspection and identification of droplet deposition states in the substrate surface. Then, the statistical relationship between the operating parameters and the quality of the deposition state was analyzed by response surface methodology. On this basis, machine learning models and intelligent recommendation frameworks for small data sets were developed to rapidly optimize operating parameters and improve the quality of thin-film coating. Optimization strategy developed by applying the principles of statistical modeling, analysis of variance, and global optimization are more efficient and less costly than traditional parameter screening methods. The experimental results show that optimum deposition quality can be obtained with the recommended operating parameters. And, validation results show a 12.8% improvement in film thickness uniformity. At the same time, no mura defects appeared on the thin-film surface. The proposed optimization strategy can improve the efficiency and quality of additive manufacturing of micro and nano thin films and is beneficial for advancing industrial applications of the electrohydrodynamic atomization coating.