Objective Framework Research Articles

Examining S-T-E-M Teachers’ Design of Integrated STEM Lesson Plans

AbstractReform efforts in different parts of the world call for the adoption of integrated approaches to STEM education. However, little is known about how teachers work, as members of learning communities, to design STEM teaching material such as lesson plans. To address this gap in the literature, we examined the design of STEM lesson plans by in-service secondary teachers (n = 26) from all S-T-E-M disciplinary backgrounds who participated as members of a learning community in the context of a 7-month professional development program comprised of 13 small-group sessions. STEM lesson plans were analysed based on the key characteristics of a conceptual framework of integrated STEM, and the teachers’ identified interdisciplinary elements were analysed through the boundary objects framework. The qualitative content analysis produced four main findings: (a) divergencies take place at the disciplinary level when teachers design STEM lesson plans; (b) teachers coming from different disciplines have different positioning regarding specific integrated STEM key characteristics; (c) the type and sequence of instructional activities served as a boundary-design practice; (d) the nature of the boundary objects that teachers identify in order to teach integrated STEM differs according to the discipline. The findings of the study shed light on teachers’ understanding and needs at the disciplinary level and call for specially designed STEM professional development programmes that provide explicit opportunities for disciplinary boundary crossings.

A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems

In recent years, there has been an increasing recognition that when machine learning (ML) algorithms are used to automate decisions, they may mistreat individuals or groups, with legal, ethical, or economic implications. Recommender systems are prominent examples of these ML systems that aid users in making decisions. The majority of past literature research on recommender systems fairness treats user and item fairness concerns independently, ignoring the fact that recommender systems function in a two-sided marketplace. In this article, we propose CP-FairRank , an optimization-based re-ranking algorithm that seamlessly integrates fairness constraints from both the consumer and producer side in a joint objective framework. The framework is generalizable and may take into account varied fairness settings based on group segmentation, recommendation model selection, and domain, which is one of its key characteristics. For instance, we demonstrate that the system may jointly increase consumer and producer fairness when (un)protected consumer groups are defined on the basis of their activity level and main-streamness , while producer groups are defined according to their popularity level. For empirical validation, through large-scale on eight datasets and four mainstream collaborative filtering recommendation models, we demonstrate that our proposed strategy is able to improve both consumer and producer fairness without compromising or very little overall recommendation quality, demonstrating the role algorithms may play in avoiding data biases. Our results on different group segmentation also indicate that the amount of improvement can vary and is dependent on group segmentation, indicating that the amount of bias produced and how much the algorithm can improve it depend on the protected group definition, a factor that, to our knowledge, has not been examined in great depth in previous studies but rather is highlighted by the results discovered in this study.

On sustainable land rent

IntroductionThis paper introduces the concept of Sustainable Land Rent (SLR), providing a comprehensive, multidimensional exploration anchored in the dynamics of its origin, separability, mobility, valuation, and the imperative for equitable distribution. SLR capitalizes on the economic mobility of land’s value to enhance community welfare and promote environmental sustainability. Advocating for the systematic institutionalization of SLR, the research tackles the complex challenge of distinguishing land value from improvements.MethodsEmploying traditional Price, Cost, and Income (PCI) methods as practiced in North America, the study addresses the technical challenge of inseparability by estimating and integrating the SLR value within each of these methods. The methodology clarifies the valuation process and establishes an objective framework for resource allocation and negotiation between public and private sectors.Results and discussionFurthermore, our findings highlight SLR’s vital role in advancing public revenue generation and underscore its function as an innovative catalyst for integrating sustainability into economic valuation models and practices in real estate development and urbanization.

Predicting suicidality with small sets of interpretable reward behavior and survey variables

The prediction of suicidal thought and behavior has met with mixed results. This study of 3,476 de-identified participants (4,019 before data exclusion) quantified the prediction of four suicidal thought and behavior (STB) variables using a short reward/aversion judgment task and a limited set of demographic and mental health surveys. The focus was to produce a simple, quick and objective framework for assessing STB that might be automatable, without the use of big data. A balanced random forest classifier performed better than a Gaussian mixture model and four standard machine learning classifiers for predicting passive suicide ideation, active suicide ideation, suicide planning and planning for safety. Accuracies ranged from 78% to 92% (optimal area under the curve between 0.80 and 0.95) without overfitting, and peak performance was observed for predicting suicide planning. The relative importance of features for prediction showed distinct weighting across judgment variables, contributing between 40% and 64% to prediction per Gini scores. Mediation/moderation analyses showed that depression, anxiety, loneliness and age variables moderated the judgment variables, indicating that the interaction of judgment with mental health and demographic indices is fundamental for the high-accuracy prediction of STB. These findings suggest the feasibility of an efficient and highly scalable system for suicide assessment, without requiring psychiatric records or neural measures. The findings suggest that STB might be understood within a cognitive framework for judgment with quantitative variables whose unique constellation separates passive and active suicidal thought (ideation) from suicide planning and planning for safety.

The study on setting priorities of zoonotic agents for medical preparedness and allocation of research resources.

The aim of this study is to develop a scoring platform to be used as a reference for both medical preparedness and research resource allocation in the prioritization of zoonoses. Using a case-control design, a comprehensive analysis of 46 zoonoses was conducted to identify factors influencing disease prioritization. This analysis provides a basis for constructing models and calculating prioritization scores for different diseases. The case group (n = 23) includes diseases that require immediate notification to health authorities within 24 hours of diagnosis. The control group (n = 23) includes diseases that do not require such immediate notification. Two different models were developed for primary disease prioritization: one model incorporated the four most commonly used prioritization criteria identified through an extensive literature review. The second model used the results of multiple logistic regression analysis to identify significant factors (with p-value less than 0.1) associated with 24-hour reporting, allowing for objective determination of disease prioritization criteria. These different modeling approaches may result in different weights and positive or negative effects of relevant factors within each model. Our study results highlight the variability of zoonotic disease information across time and geographic regions. It provides an objective platform to rank zoonoses and highlights the critical need for regular updates in the prioritization process to ensure timely preparedness. This study successfully established an objective framework for assessing the importance of zoonotic diseases. From a government perspective, it advocates applying principles that consider disease characteristics and medical resource preparedness in prioritization. The results of this study also emphasize the need for dynamic prioritization to effectively improve preparedness to prevent and control disease.

Trade-offs between Farm Incomes and Soil Loss: An Application of Bio-economic Modelling in a Semi-Arid Watershed in South India

Soil erosion is an economic problem and the cost of soil loss and its consequences could be very harsh. The major threat facing the sustainability and productivity is erosion and the associated nutrient loss through run off. And climatic shocks like drought and floods also aggravate the soil loss Aims: The study aims to bring out the different plans and the tradeoff analysis of the conservation and degradation through a multi objective framework. Study Design: Primary data was collected from the farmers of the Padmaram watershed, in Mahbubnagaar district, Telangana state in south India. A detailed questionnaire was prepared which included the current management practices, the input costs and output prices associated. And the soil loss was estimated using the RUSLE equation. And the tradeoffs were obtained by the multiobjective linear programming (MOLP) Place and Duration of Study: The padmaram watershed was selected, from kondurg mandal of Mahabubnagar district of Telanagana. Methodology: The multi–objective linear programming MOLP, is employed to get an efficient solution where in conflicting objectives are simultaneously optimized subject to constraints. The soil loss under different climatic scenarios were modelled using the CMIP data for 2020s,2050s and 2080s. The impact of climate change on soil loss and farm incomes were also assessed. Results: The operational land holding of a small farmer was 1.32 ha, medium famer about 2.71 ha and a large farmer about 4.99 ha. Cotton and maize were two major crops grown in the watershed holding an area of 56 per cent followed by paddy which occupies about 15 per cent. The major rabi crops were maize and rabi paddy (9 %). The cropping intensity of the watershed was 116.87 per cent. The soil loss from 60.0% of the watershed area was below 3.0 t ha−1 y−1. The soil loss from 27.5% area ranged from 3.1 to 4.5 t ha−1 y−1 and remaining 12.5% area have soil loss more than 4.6 t ha−1 y−1. Soil loss and net returns for future climate scenarios were assessed. Conclusion: The analysis of trade off between production and conservation would be useful in identifying optimum crop plans with reduction in soil loss. The results stress that the interventions in agriculture have varying costs and environmental and economic impacts. Their implementation requires appropriate investment decisions.

Zero Leprosy Strategic Plans: Global and National Level: A Short Communication

Since the launch of multidrug therapy in 1982, the global leprosy situation has undergone significant changes, with the prevalence falling from over 5 million cases in the mid-1980s to <200,000 by the end of 2016. In India, the program reduced the prevalence rate from 57.8/10,000 in 1983 to <1/10,000 by the end of 2005, marking the achievement of the World Health Organizations (WHOs) target of eliminating leprosy as a public health issue. The WHO has recently introduced a new global leprosy plan called “Towards Zero Leprosy” as part of the disease-specific strategies outlined in the WHO's roadmap for neglected tropical diseases (NTDs) 2021–2030. This strategy aims to achieve interruption of leprosy transmission by 2030 and provides a framework for direction, objectives, challenges, and strategic pillars at a global level. In India, a committee of over 80 experts in the field of leprosy developed the National Strategic Plan and Roadmap for Leprosy 2023–2027, which was published on January 30, 2023. This policy is aligned with the WHO's NTDs roadmap 2021–2030 and the Global Leprosy Strategy 2021–2030, both of which aim to interrupt leprosy transmission by 2030. In this article, we have discussed the strategic plans of both the National leprosy eradication programme (NLEP) and the WHO.

Preoperative difficulty factors in delayed laparoscopic cholecystectomy: Tokyo Guidelines 2018 surgical difficulty score analysis.

Tokyo Guidelines 2018 (TG18) recommend early laparoscopic cholecystectomy (LC) for low-risk acute cholecystitis (AC); however, some patients undergo delayed LC (DLC) after conservative treatment. DLC, influenced by chronic inflammation, is a difficult procedure. Previous studies on LC difficulty lacked objective measures. Recently, TG18 introduced a novel 25 findings difficulty score, which objectively assesses intraoperative factors. The purpose of this study was to use the difficulty score proposed in TG18 to identify and investigate the predictors of preoperative high-difficulty cases of DLC for AC. We retrospectively reviewed 100 patients with DLC after conservative AC treatment. The surgical difficulty of DLC was evaluated using a difficulty score. Based on previous studies, the highest scores in each category were categorized as grades A-C. The severity of AC was mild in 51 patients and moderate in 49. Surgical outcomes revealed a distribution of difficulty scores, with grade C indicating high difficulty, showing significant differences in operative time, blood loss, achieving a critical view of safety, bailout procedures, and postoperative hospital stay compared with grades A and B. Regarding the preoperative risk factors, multivariate analysis identified age >61 years (p = .008), body mass index >27.0 kg/m2 (p = .007), and gallbladder wall thickness >6.2 mm (p = .001) as independent risk factors for grade C in DLC. The difficulty score proposed in TG18 provides an objective framework for evaluating surgical difficulty, allowing for more accurate risk assessments and improved preoperative planning in DLC for AC.

Community-driven design: Collaborative objects, community-generated content, and the expanded role of architects

This article explores the intersection of digital architecture, collaborative creativity, and community engagement through our conceptual framework of Collaborative Objects. By enabling a shift from user-generated content to community-generated content in architecture, this new approach promotes democratic design processes. Practical illustrations are provided through a series of public case studies—H = N Co-Doodle, H = N BLOCK, and Meta Block Linz—highlighting the versatility of the Collaborative Objects framework embedded within expanded software applications. These studies address the formal and informal consequences, opportunities, and challenges of transforming static design elements into dynamic, community driven narratives. While recognizing the challenges inherent in this paradigm shift, the article concludes by emphasizing the compelling potential for more shared, inclusive, and community-oriented design processes in the architectural field.

An objective skill assessment framework for microsurgical anastomosis based on ALI scores

IntroductionThe current assessment and standardization of microsurgical skills are subjective, posing challenges in reliable skill evaluation. We aim to address these limitations by developing a quantitative and objective framework for accurately assessing and enhancing microsurgical anastomosis skills among surgical trainees. We hypothesize that this framework can differentiate the proficiency levels of microsurgeons, aligning with subjective assessments based on the ALI score.MethodsWe select relevant performance metrics from the literature on laparoscopic skill assessment and human motor control studies, focusing on time, instrument kinematics, and tactile information. This information is measured and estimated by a set of sensors, including cameras, a motion capture system, and tactile sensors. The recorded data is analyzed offline using our proposed evaluation framework. Our study involves 12 participants of different ages (35.42±9.78\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$35.42\\pm 9.78$$\\end{document} years) and genders (nine males and three females), including six novice and six intermediate subjects, who perform surgical anastomosis procedures on a chicken leg model.ResultsWe show that the proposed set of objective and quantitative metrics to assess skill proficiency aligns with subjective evaluations, particularly the ALI score method, and can effectively differentiate novices from more proficient microsurgeons. Furthermore, we find statistically significant disparities, where microsurgeons with intermediate level of skill proficiency surpassed novices in both task speed, reduced idle time, and smoother, briefer hand displacements.ConclusionThe framework enables accurate skill assessment and provides objective feedback for improving microsurgical anastomosis skills among surgical trainees. By overcoming the subjectivity and limitations of current assessment methods, our approach contributes to the advancement of surgical education and the development of aspiring microsurgeons. Furthermore, our framework emerges to precisely distinguish and classify proficiency levels (novice and intermediate) exhibited by microsurgeons.

Development of a management-based ranking of beaches

Beach rankings are very frequent on the internet; however, the information provided on how these rankings are made is often unclear and their content is mostly subjective. In addition, the vast majority of these rankings do not take into account the fact that beaches are coastal eco-systems. The aim of the research was to develop an objective framework to rank the quality of beaches worldwide. The framework integrates indicators to assess the socio-ecological system quality and can be used as a basis for effective beach management. The methodology involved the collection, evaluation and grouping of indicators into domains and categories. Moreover, a measurement technique and a 5-point rating score for each indicator was used. Weights were calculated for different beach types using an analytical hierarchical process and the methodology was validated by a focus group of beach management experts. The quality value of each beach was calculated through equations and the results were presented in graphs inspired by the Circles of Sustainability and the Ocean Health Index. The theoretical application was tested on Portuguese beaches. The framework presents a holistic assessment of four domains: Recreation, Protection, Conservation and Sanitary. The resulting Beach Ranking Framework (BRF) is an objective, holistic framework designed to communicate with society, unlike the existing beach quality assessments.

Optimal distributed generators allocation with various load models under load growth using a meta-heuristic technique

Distribution network planning and operation are facing several problems, including asset congestion, voltage fluctuations, and system instability. The adequate planning and modeling of distributed generators and capacitor banks must quantify these problems. This article presents the optimal allocation of distributed generators in parallel with capacitor banks in distribution networks with single and multi-objectives using the Gazelle Optimization Algorithm (GOA) and Mountain Gazelle Optimization Algorithm (MGOA). The single objective framework includes technical objectives like minimization of active power losses. The multi-objective framework includes technical and non-technical objectives like simultaneously minimization of active power losses, voltage stability, and voltage deviation, and minimization of polluting greenhouse gases and total electricity purchase cost. Furthermore, these planning problems are investigated by three case studies on different nonlinear voltage-dependent models at two different loading conditions from future planning perspectives. The effectiveness and feasibility of the MGOA are evaluated on the IEEE standard 33 bus system. As a result, the MGOA demonstrates a remarkable reduction in technical and non-technical objectives in all types of distributed generator placement. Moreover, a comparative analysis of other existing research works validated the efficiency and feasibility of established algorithms at each use case with different load models by improving all the objective functions of network planning. In single-objective and multi-objective frameworks, the active power losses reduce to 94.42% and 93.57% in the voltage-independent model, respectively. Meanwhile, the non-technical objectives are also significantly improved for each load model, further validating the efficiency of the proposed algorithms.

Empirical Bayes factors for common hypothesis tests.

Bayes factors for composite hypotheses have difficulty in encoding vague prior knowledge, as improper priors cannot be used and objective priors may be subjectively unreasonable. To address these issues I revisit the posterior Bayes factor, in which the posterior distribution from the data at hand is re-used in the Bayes factor for the same data. I argue that this is biased when calibrated against proper Bayes factors, but propose adjustments to allow interpretation on the same scale. In the important case of a regular normal model, the bias in log scale is half the number of parameters. The resulting empirical Bayes factor is closely related to the widely applicable information criterion. I develop test-based empirical Bayes factors for several standard tests and propose an extension to multiple testing closely related to the optimal discovery procedure. When only a P-value is available, an approximate empirical Bayes factor is 10p. I propose interpreting the strength of Bayes factors on a logarithmic scale with base 3.73, reflecting the sharpest distinction between weaker and stronger belief. This provides an objective framework for interpreting statistical evidence, and realises a Bayesian/frequentist compromise.

An Objective Framework for Bivariate Risk Analysis of Flash Floods Under the Compound Effect of Rainfall Characteristics

An Objective Framework for Bivariate Risk Analysis of Flash Floods Under the Compound Effect of Rainfall Characteristics

Navigating the AI Revolution: Implications for Business Education and Pedagogy

Generative artificial intelligence (AI) is rapidly emerging as a transformative force across various sectors. As education shifts towards an AI-focused future, adapting teaching methodologies and evaluation strategies to this technological evolution becomes more and more important. This paper delves into the profound implications of generative AI on business education, critically analyzing its influence on broad program learning outcomes as well as on specific assessment tasks, ranging from quizzes to work-integrated learning projects. By examining and assessing responses from ChatGPT, we evaluate their structural coherence and the potential to enhance or replace key skills evaluated in students. Our findings primarily indicate that for formulaic quizzes and short essay questions, GPT-4 often delivers accurate solutions. In the context of research reports and reflection journals, GPT-4 serves more as a guide and scaffolding tool. As AI continues to advance, it becomes increasingly crucial for business educators to re-examine their learning objective frameworks. This includes utilizing the technology’s potential while navigating its complexities, ensuring that education remains effective, relevant, and in step with the pace of innovation.

A framework to classify gestational diabetes diagnosed by routine antenatal 75g glucose tolerance testing

Objective To create an objective framework to classify gestational diabetes mellitus diagnosed by routine antenatal 75 g diabetes testing results to provide an alternative to current treatment-based classification. Methods A framework was created to classify gestational diabetes according to the severity of glycemic abnormalities after routine antenatal 75 g GTT (classes 1 through 4, determined by fasting and post-test glycemic abnormalities). A retrospective cohort chart review was used to correlate clinically how often diet therapy alone maintained glycemic targets throughout pregnancy in each class. Chi-square analysis was used to assess inter-class differences in the success of diet therapy alone maintaining glycemic targets throughout pregnancy. Results Seventy-four of 228 (33%), 35/228 (15%), 76/228 (33%), and 43/228 (19%) of the study population were classified as Class 1, 2, 3, or 4, respectively. Of eighty-nine patients who maintained glycemic targets throughout pregnancy with diet alone 51/89 (57%) were Class 1, 20/89 (22.5%) were Class 2, 11/89 (12.5%) were Class 3, and 7/89 (8%) were Class 4. Chi-square analysis showed statistically significant inter-class differences in the likelihood of diet therapy alone maintaining glycemic targets throughout pregnancy. Conclusion In this framework classifying gestational diabetes according to the severity of glycemic abnormalities after routine antenatal 75 g GTT (an objective proxy for disease severity), the higher the assigned class, the less likely that diet therapy alone maintained glycemic targets throughout pregnancy (a clinical proxy for disease severity).

ОЦІНКА ЕКОЛОГІЧНИХ ЗБИТКІВ ВІД ВИКИДІВ ТРАНСПОРТНО-ЛОГІСТИЧНИХ ЗАСОБІВ В КОНТЕКСТІ СТАЛОГО РОЗВИТКУ

This article thoroughly examines the environmental damage caused by emis-sions from transportation and logistics vehicles in Odesa, within the framework of sustai-nable development objectives. It presents a detailed analysis of the main pollutants emitted by vehicles using gasoline, diesel, and liquefied petroleum gas, underscoring the significant contribution these emissions make to both local and global environmental issues. The study highlights the substantial volumes of greenhouse gases produced and the current lack of economic regulatory mechanisms to manage them effectively. Comparative analysis of global environmental tax rates on CO2 demonstrates Ukraine's position as having the lowest rate, suggesting inadequate regulatory measures. Through methodical calculations, it was deter-mined that Odesa's total CO2 emissions for the year 2022 amounted to 1,089,421.9 tons. This resulted in an estimated environmental damage cost of $893,326 USD, which remains uncompensated due to regulatory shortcomings.The findings emphasize the necessity of integrating such emission data into environ-mental damage assessments and implementing robust economic instruments to regulate emissions. Additionally, the paper explores strategic approaches to enhance emission regu-lations, including the potential introduction of more stringent environmental taxes or cap-and-trade systems, and suggests ways to align these with international environmental stan-dards. The broader implications of such regulatory adjustments are discussed, particularly their role in facilitating sustainable urban development. This comprehensive analysis aims to provide policymakers and urban planners with an understanding of the environmental stakes involved and practical steps towards achieving sustainability in urban transportation systems. By comparing Ukraine’s policies to international standards, the research offers valuable insights into potential reforms that can drive the sustainable future for Ukraine. Keywords: transportation and logistics vehicles, environmental damage, sustainable development, greenhouse gases, emission regulation, environmental tax.

Using Data Analysis to Improve the Teaching Quality of Civics in College English Courses

Abstract College English courses, serving both instrumental and humanistic purposes, are a crucial component of general education at colleges and universities. This underscores the pressing need to advance the integration of civics and politics within these courses. This study develops a blended teaching model for incorporating civics and politics into college English courses, employing a structured pedagogical framework of teaching objectives, implementation, and evaluation. It proposes several hypotheses concerning factors that influence the quality of civics and politics instruction within English courses. Using School Y as a case study, a comparative experiment was conducted, and various data analysis techniques— including one-way ANOVA, multiple linear regression, and stepwise regression—were applied to substantiate the hypotheses. The experimental results revealed that students in class A showed an improvement ranging from 5.59 to 9.5683 points over class B. Correlation and regression analyses demonstrated that the selected predictor variables accounted for 88.3% of the variance in teaching quality, with an F-value of 231.158 and a p-value of 0.000, indicating a robust fit and statistical significance of the regression model. All hypotheses were confirmed except for H2, H6, and H7. This research delineates the varying impacts of each factor on teaching quality. It offers concrete recommendations for enhancing civics and politics education in university English courses through a blended learning approach.

Une approche par l’objet-frontière de la production collective de l’innovation sociale. L’exemple de la plateforme touristique Les Oiseaux De Passage.

This article looks at the process of collective production of social innovation involving a diversity of actors and organizations. It mobilizes the conceptual framework of the boundary object integrated into a dynamic social infrastructure, enabling the articulation of logics stemming from the strata of creative ecology (underground and upperground). The research is based on a qualitative study of “Les Oiseaux De Passage” platform, whose ambition is to offer alternative tourism based on storytelling and hospitality. The results show that a digital platform co-constructed by a creative collective from different social worlds, organized as a cooperative, can play the role of a facilitating boundary object.

National projects of the Alash intelligence in the field of education

The article examines the foundations of the statehood of Alash autonomy, defined in the draft program of the national political party ”Alash”. The idea of establishing Kazakh statehood served as the foundation for several politically significant problems pertaining to the interests of the people in the party program. Prosperity was the route that the Alash intelligence suggested for the establishment of a state. The Science-Education agenda of the Alash party, as outlined in Section IX, provides the theoretical framework for the national educational objectives of intellectuals. Alash leaders carried out national projects for the advancement of science and education in the nation even after the Soviet Union came into power. Alash intellectuals focused a lot of attention on educational reform in the 1920s. In an effort to raise educational standards, Alash intellectuals formed new model schools and wrote the requisite manuals for all of the country's schools. The National Intelligence, which linked the new alphabet issue to education, also gave it some thought in the 1920s. It was essential to provide education on a national scale because Kazakh students attend national schools. Thus, the class ideology of the Bolsheviks and the nationalist ideology of the Alash engaged in an unyielding conflict during the early years of the Soviet regime.