Problem-solving Capabilities Research Articles

Potential Safety Issues and Moral Hazard Posed by Artificial General Intelligence

Abstract. Artificial Intelligence (AI), a technology with a wide range of intelligence capabilities, has developed rapidly in recent years, bringing significant convenience and efficiency to society. However, most of the current artificial intelligence technologies belong to narrow artificial intelligence. Unlike Narrow AI, Artificial General Intelligence (AGI) possesses a more comprehensive understanding and problem-solving capability. AGI can learn in an unsupervised manner. General artificial intelligence can not only stand out in specific fields. It can also make effective decisions to a certain extent and operate in a wide range of environments. However, rapid progress has also raised widespread concerns about its potential risks. Therefore, the development of artificial intelligence requires standardization, which is urgent to ensure that it can make decisions that benefit humanity. Based on existing literature and data results, this paper explores the security issues and moral risks that general artificial intelligence may bring to humans. The research results indicate that these risks include user privacy breaches, system security issues, and social ethical conflicts. Dealing with these risks requires the joint efforts of all practitioners. This includes developing AGI in an ethical manner and ensuring that AI does not engage in activities that violate human interests.

Exploring Climate Change Problem-Solving Skills Among Junior High Students: An Empirical Analysis from Madura

This study aims to explore the problem-solving skills of junior high school students in Madura in relation to climate change issues. Understanding these skills is crucial for preparing students to tackle environmental challenges effectively.Methodology A descriptive quantitative approach was employed, collecting data from 63 students through a set of problem-solving skill indicators. The indicators measured various aspects of students' abilities to understand, devise plans, carry out those plans, and reflect on their solutions to climate-related problems. The results indicate that 73% of students fall into the low category of problem-solving skills, while only 27% demonstrate moderate skills. Notably, no students achieved high or very high ratings. Specific analysis revealed that students exhibited a moderate understanding of problems but showed low performance in devising plans, executing those plans, and reflecting on their outcomes. These findings highlight significant gaps in students' problem-solving capabilities concerning climate change. The low proficiency across various indicators suggests a need for enhanced educational strategies to develop these essential skills. The analysis indicates that without targeted interventions, students may struggle to effectively address the complexities of climate issues.

Metadata schema for virtual building models in digital twins: VB schema implemented in GPT-based applications

A virtual building model (VBM) is a virtual entity that represents the physical behavior of a target building mathematically within a digital twin environment. The creation and synchronization of a VBM are achieved by utilizing various interrelated virtual sub-models, including behavior, correction, and distance models. To achieve continuous digital twinning, it is essential to manage the VBM with virtual sub-models. However, these metadata schemas have limitations in describing VBMs representing operational building behaviors within the concept of building digital twins (DTs). Therefore, this study proposes a novel metadata schema, termed the virtual building model metadata schema (VB schema), to represent and manage VBMs in DT-built environments. The VB schema is established according to the mathematical and semantic ontology of the in-situ modeling and calibration approach for constructing and correcting virtual models during building operations, and it is linked to physical entities, data, and applications within DTs. Specifically, it involves: (1) determining classes for operational data and virtual models; (2) establishing relationships for interactions between model and data entities, between model classes, between model and physical entities, and between model and applications; (3) defining properties for each class of models; and (4) extending into the exiting metadata schema of Brick. To demonstrate the proposed VB schema, a virtual model describing supply pressure behaviors in a central heating system was developed and represented using the VB schema for DT-enabled building operations. Additionally, the VB schema was utilized for implementing generative pre-trained transformer (GPT)-based DT applications, which highlights its benefits in enhancing ontology comprehension of DTs in the context of VBMs, improving autonomous problem-solving capabilities in real building systems, and providing better interpretation of application results compared to cases where only the Brick schema was used. The VB schema is expected to enable continuous and autonomous in-situ management of VBMs for intelligent building services within the DT.

Thriving minds, thriving workplaces: unleashing creativity through psychological wellbeing and psychological capital

PurposeThis study examines the complicated relationship between psychological wellbeing, joy at work, creative ability and the mediating influence of psychological capital, drawing insights from self-determination theory within the context of the banking sector.Design/methodology/approachThe study utilized random sampling to gather comprehensive data from 465 banking employees. Following data collection, structural equation modeling was employed to test the hypotheses formulated based on the collected data.FindingsFindings underscore a significant association between psychological wellbeing and both joy at work and creative ability within the banking sector. Moreover, the study elucidates that psychological capital acts as a crucial mediator, illuminating the pathway through which psychological wellbeing influences joy at work and subsequently enhances creative ability.Practical implicationsThis research offers valuable insights for organizational leaders and policymakers, emphasizing the imperative of prioritizing psychological wellbeing initiatives and nurturing positive work environments to enhance employee satisfaction, productivity and innovative contributions within the banking sector.Originality/valueThe application of self-determination theory as a theoretical framework provides a robust foundation for understanding the dynamics between psychological factors and workplace outcomes. The banking industry, often characterized by high stress levels and demanding work environments, stands to benefit substantially from interventions aimed at fostering psychological wellbeing. By cultivating positive mental health and capitalizing on joy at work, organizations can stimulate employee creativity ability, thereby fostering innovation and adaptive problem-solving capabilities crucial in the contemporary banking landscape.

Strategic leadership for complex energy and oil & gas projects: A conceptual approach

Strategic leadership is essential for the success of complex energy and oil & gas projects, particularly those that involve large-scale, multidisciplinary teams navigating dynamic environments. This paper outlines a conceptual framework designed to enhance leadership effectiveness in managing such projects. The framework emphasises three key pillars: collaboration, stakeholder engagement, and regulatory alignment, which are critical for driving innovation and achieving operational success in the energy sector. In the context of collaboration, the framework advocates for fostering an inclusive team culture that encourages knowledge sharing and interdisciplinary cooperation. Effective leaders must cultivate relationships among diverse stakeholders, ensuring that varied perspectives are integrated into project planning and execution. This collaborative approach not only enhances problem-solving capabilities but also promotes a shared sense of ownership among team members. Stakeholder engagement is another crucial element of this leadership framework. Leaders must actively communicate with stakeholders—including government entities, local communities, and industry partners—to build trust and facilitate transparency. By understanding the needs and concerns of different stakeholders, leaders can align project objectives with broader societal and environmental goals, thereby minimizing resistance and enhancing project viability. Regulatory alignment is vital in the energy and oil & gas sectors, where compliance with industry regulations and environmental standards is paramount. The proposed framework guides leaders in navigating complex regulatory landscapes, ensuring that project strategies are aligned with legal requirements and best practices. This proactive approach mitigates risks associated with non-compliance, ultimately safeguarding the project's reputation and long-term sustainability. By integrating these pillars into their leadership practices, energy and oil & gas project leaders can drive innovation and operational excellence, even under challenging conditions. This conceptual approach underscores the importance of strategic leadership in managing complexity and achieving successful outcomes in the energy sector, paving the way for future advancements in project management methodologies. Keywords: Strategic Leadership, Energy Projects, Oil & Gas, Collaboration, Stakeholder Engagement, Regulatory Alignment, Operational Success, Innovation, Project Management Framework.

Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique

Cloud computing has revolutionized the way businesses and individuals access and utilize computing resources. Efficient virtual machine placement is a critical aspect of optimizing resource utilization, reducing operational costs, energy consumption, service level agreement and minimum virtual machine migrations, execution time, and ensuring the overall performance of cloud services. This manuscript introduces a novel approach that combines the creative problem-solving capabilities of brainstorming with the computational power of Artificial Neural Networks (ANN) to address the virtual machine placement problem in cloud environments. In this study, we propose a hybrid technique that leverages the collective intelligence of human brainstorming to generate a diverse set of placement strategies. These strategies are then evaluated, refined, and optimized using an ANN model trained on historical cloud resource allocation workload logs. By integrating the human creative process with the data-driven predictive capabilities of ANN, our approach aims to overcome the limitations of traditional virtual machine placement algorithms, which often struggle to adapt to dynamic workloads and changing resource requirements. The manuscript provides a detailed description of the hybrid technique, including the process of brainstorming for generating placement strategies, data collection and preprocessing, ANN model development, and the integration of these components into an efficient placement system. We present experimental results demonstrating the effectiveness of our approach in optimizing resource allocation, improving service performance, and optimizing resource utilization, reducing energy consumption, service level agreement and minimum virtual machine migrations, reducing execution time compared to existing static, and meta-heuristic methods. The proposed Brain Storming with ANN based Hybrid Technique offers a promising solution for enhancing the efficiency of virtual machine placement in cloud computing environments. BSO-ANN outperforms the existing techniques using performance metrics (energy consumption(Kwh), execution time(ms), SLA violations, and number of migrations). It combines human ingenuity with data-driven insights to adapt to the ever-changing dynamics of cloud workloads. This manuscript contributes to the ongoing research in cloud resource management, offering a practical approach for cloud service providers and organizations to better utilize their resources and enhance the overall quality of cloud-based services. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd.

Q-Learning-Based Dumbo Octopus Algorithm for Parameter Tuning of Fractional-Order PID Controller for AVR Systems

The tuning of fractional-order proportional-integral-derivative (FOPID) controllers for automatic voltage regulator (AVR) systems presents a complex challenge, necessitating the solution of real-order integral and differential equations. This study introduces the Dumbo Octopus Algorithm (DOA), a novel metaheuristic inspired by machine learning with animal behaviors, as an innovative approach to address this issue. For the first time, the DOA is invented and employed to optimize FOPID parameters, and its performance is rigorously evaluated against 11 existing metaheuristic algorithms using 23 classical benchmark functions and CEC2019 test sets. The evaluation includes a comprehensive quantitative analysis and qualitative analysis. Statistical significance was assessed using the Friedman’s test, highlighting the superior performance of the DOA compared to competing algorithms. To further validate its effectiveness, the DOA was applied to the FOPID parameter tuning of an AVR system, demonstrating exceptional performance in practical engineering applications. The results indicate that the DOA outperforms other algorithms in terms of convergence accuracy, robustness, and practical problem-solving capability. This establishes the DOA as a superior and promising solution for complex optimization problems, offering significant advancements in the tuning of FOPID for AVR systems.

Advancements and Challenges in Artificial Intelligence Applications: A Comprehensive Review

This comprehensive review explores the advancements and challenges in the field of Artificial Intelligence (AI) applications across various industries. With rapid technological progress, AI has transformed domains such as healthcare, finance, education, and manufacturing, driving innovations in automation, data analysis, and decision-making. This examines the Important and significant AI technologies, including machine learning, natural language processing, and computer vision, highlighting their contributions to enhancing efficiency, accuracy, and problem-solving capabilities, these advancements, several challenges persist, such as ethical concerns, bias in AI systems, data privacy issues, and the need for robust regulatory frameworks. Additionally, the complexity of integrating AI into existing systems and workforce displacement due to automation are critical barriers to widespread adoption and provides a balanced perspective on both the potential and limitations of AI, offering insights into future research directions and strategies for addressing these challenges to ensure the sustainable and ethical development of AI technologies.

Analysis Mathematical Resilience Relationship With Mathematical Problem Solving Ability in Functions Derivative Material

This study is a correlational research aimed at exploring the relationship between mathematical resilience and problem-solving skills in mathematics. Conducted at a senior high school in Karawang, the research involved a sample of 36 students. The method employed was ex post facto, with purposive sampling to select participants based on specific criteria. The instruments used included a test on mathematical problem-solving abilities focused on derivative functions and a scale measuring mathematical resilience. Findings revealed a significant correlation of 0.819 between mathematical resilience and problem-solving abilities, while a correlation of 0.181 was attributed to other factors like conceptual understanding. Students with strong resilience and a solid grasp of concepts typically find it easier to tackle mathematical problems. Conversely, those with weaker resilience and limited understanding of the concepts struggle to comprehend the questions and develop effective strategies, leading to lower problem-solving capabilities. In conclusion, a notable relationship exists between mathematical resilience and students' ability to solve mathematical problems.

Personalized food consumption detection with deep learning and Inertial Measurement Unit sensor

For individuals diagnosed with diabetes mellitus, it is crucial to keep a record of the carbohydrates consumed during meals, as this should be done at least three times daily, amounting to an average of six meals. Unfortunately, many individuals tend to overlook this essential task. For those who use an artificial pancreas, carbohydrate intake proves to be a critical factor, as it can activate the insulin pump in the artificial pancreas to deliver insulin to the body. To address this need, we have developed personalized deep learning model that can accurately detect carbohydrate intake with a high degree of accuracy. Our study employed a publicly available dataset gathered by an Inertial Measurement Unit (IMU), which included accelerometer and gyroscope data. The data was sampled at a rate of 15 Hz, necessitating preprocessing. For our tailored to the patient model, we utilized a recurrent network comprising Long short-term memory (LSTM) layers. Our findings revealed a median F1 score of 0.99, indicating a high level of accuracy. Additionally, the confusion matrix displayed a difference of only 6 s, further validating the model’s accuracy. Therefore, we can confidently assert that our model architecture exhibits a high degree of accuracy. Our model performed well above 90% on the dataset, with most results between 98%–99%. The recurrent networks improved the problem-solving capabilities significantly, though some outliers remained. The model’s average prediction latency was 5.5 s, suggesting that later meal predictions result in extended meal progress predictions. The dataset’s limitation of mostly single-day data points raises questions about multi-day performance, which could be explored by collecting multi-day data, including night periods. Future enhancements might involve transformer networks and shorter time windows to improve model responsiveness and accuracy. Therefore, we can confidently assert that our model exhibits a high degree of accuracy.

Emergent Model Based On Hybrid Rough Sets Systems

The development of many knowledge discovery methods provided us with a good foundation for building hybrid systems based on rough set theory for knowledge discovery in a database. The need for more effective methods of generating and maintaining global nonfunctional properties suggests an approach analogous to those of natural processes in generating emergent properties. An emergent model allows the constraints of the task to be represented more naturally and permits only pertinent task-specific knowledge to emerge in the course of solving the problem. This paper describes some basics of emergent phenomena and its implementation in the hybrid system of rough sets combined with other methods. Further, demonstrations and guidelines are presented on how to exploit emergent intelligence and extend the problem-solving capabilities of these hybrid systems.

Improved multi-strategy adaptive Grey Wolf Optimization for practical engineering applications and high-dimensional problem solving

The Grey Wolf Optimization (GWO) is a highly effective meta-heuristic algorithm leveraging swarm intelligence to tackle real-world optimization problems. However, when confronted with large-scale problems, GWO encounters hurdles in convergence speed and problem-solving capabilities. To address this, we propose an Improved Adaptive Grey Wolf Optimization (IAGWO), which significantly enhances exploration of the search space through refined search mechanisms and adaptive strategy. Primarily, we introduce the incorporation of velocity and the Inverse Multiquadratic Function (IMF) into the search mechanism. This integration not only accelerates convergence speed but also maintains accuracy. Secondly, we implement an adaptive strategy for population updates, enhancing the algorithm's search and optimization capabilities dynamically. The efficacy of our proposed IAGWO is demonstrated through comparative experiments conducted on benchmark test sets, including CEC 2017, CEC 2020, CEC 2022, and CEC 2013 large-scale global optimization suites. At CEC2017, CEC 2020 (10/20 dimensions), CEC 2022 (10/20 dimensions), and CEC 2013, respectively, it outperformed other comparative algorithms by 88.2%, 91.5%, 85.4%, 96.2%, 97.4%, and 97.2%. Results affirm that our algorithm surpasses state-of-the-art approaches in addressing large-scale problems. Moreover, we showcase the broad application potential of the algorithm by successfully solving 19 real-world engineering challenges.

Comparative Analysis of Intelligence and Problem-Solving Abilities in Corvids and Primates

Abstract: This paper explores the intelligence and problem-solving capabilities of corvids (family Corvidae, including crows, ravens, and magpies) and primates (order Primates, including monkeys, apes, and humans). By reviewing empirical studies, cognitive tests, and observed behaviours, this research aims to highlight the cognitive processes and adaptive strategies that these two groups employ. The research compares tool use, social learning, memory, and problem-solving skills, emphasizing the convergent evolution of intelligence. The findings are supported by data presented in tables and graphs to illustrate key points.

Exploring The Boundaries of Uncertainty: Interval Valued Pythagorean Neutrosophic Set and Their Properties

The interval-valued Pythagorean fuzzy set (IVPFS) presents a novel approach to tackling vagueness and uncertainty, while neutrosophic sets, a broader concept encompassing of fuzzy sets and intuitionistic fuzzy sets, are tailored to depict real-world data characterized by uncertainty, imprecision, inconsistency, and incompleteness. Additionally, the development of Interval Value Neutrosophic Sets (IVNS) enhances precision in handling problems involving a range of numbers within the real unit interval, rather than focusing solely on a single value. However, despite these advancements, there is a deficiency in research addressing practical implementation challenges, conducting comparative analyses with existing methods, and applying these concepts across various fields. This study aims to bridge this research gap by proposing a novel concept based on the Interval Valued Pythagorean Neutrosophic Set (IVPNS), which is a generalization of the IVPFS and INS. The development of IVPNS provides a more comprehensive framework for handling uncertainty, ambiguity, and incomplete information in various fields, leading to more robust decision-making processes, improved problem-solving capabilities, and better management of complex systems. Furthermore, this research introduces the algebraic operations for IVPNS, including addition, multiplication, scalar multiplication, and exponentiation and provides a comparative analysis with IVPFS and IVNS. The study incorporates illustrative numerical examples to demonstrate these operations in practice. Additionally, this study provides and rigorously proves the algebraic properties of IVPNS, specifically discussing their commutative and associative properties. This validation ensures compliance with established conditions for IVPNS, reinforcing their theoretical soundness and practical applicability.

Digitally powered solution delivery: The use of IoT and AI for transitioning towards a solution business model

Digitalization has played a key role in enabling the delivery of service-based solutions by facilitating customer-oriented needs-finding and personalized problem-solving capabilities. Artificial intelligence (AI) and Internet-of-Things (IoT) are decisive technologies in this process. This study evaluates how the use AI and IoT platforms impact solution delivery business models among servitized and non-servitized businesses. By running regression models on a sample of 213 Spanish businesses for 2023, it was found that the use of IoT platforms, rather than AI platforms, significantly assist companies down the analyzed solution delivery dimensions (i.e., customer embeddedness, operational adaptiveness, offering integratedness, and organizational networkedness). Additionally, results indicate that the combined use of IoT platforms when servitizing leads to greater customer embeddedness, whereas the use of AI platforms among servitized firms significantly contributes to improving operational adaptiveness.

Evaluation of the Fathers’ and Men’s Group project formed by Turkish immigrants in Germany

ABSTRACT Social workers engaged in combating violence and possessing expertise in group facilitation should demonstrate a thorough comprehension of various group methodologies and be acquainted with exemplar instances of good practice. This article delineates the Fathers’ and Men’s Group Project, a support group run by Turkish immigrants in Germany since its inception 17 years ago, subsequently evolving into an anti-violence support group recognized nationwide. The paper investigates the manner in which fathers encountering diverse challenges and requirements receive support, which is demonstrated through a practical example. The support group facilitates the provision of assistance to men by individuals sharing comparable experiences, thereby enhancing their social support networks, refining their problem-solving capabilities, and promoting awareness regarding violence. The objective of this paper is to equip social work instructors, students, and graduates with an understanding of the dynamics, functioning, and characteristics of support groups.

Student-led discussions for sustainability education: an autoethnographic exploration

Purpose The purpose of this paper is to explore an interactive teaching approach using an autoethnographic lens to enhance key competencies in sustainability. These competencies are integrated sets of knowledge, skills, and attitudes that facilitate effective task performance and problem-solving related to real-world sustainabiloity issues (Wiek, Withycombe, and Redman, 2011). Focusing on interpersonal competencies in higher education for sustainable development (HESD), this paper emphasiizes the possibilities of student-led discussions and self-reflection to inspire, support, and guide collaborative and participatory learning and problem-solving (Brundiers et al., 2020). Shifting from traditional lectures to student-led discussions transforms instructors into facilitators, showcasing the potential of this method. Design/methodology/approach Using an autoethnographic lens, this paper explores the student-led discussion approach to support the development of interpersonal competencies in the realm of the key competencies in HESD. Drawing from a blend of relevant literature and the author’s personal experiences spanning multiple years with this approach, this paper offers insights into its benefits and effective implementation. The student-led discussion approach involves the following: Assignment of relevant texts: University students are tasked with reading an engaging and pertinent piece of literature as homework; Written reading reflection: Students produce written reflections on the assigned reading in anticipation of a subsequent in-class discussion; In-class discussion: Each student arrives prepared to potentially lead the conversation. At the onset of the session, three student facilitator names are randomly selected. These chosen individuals bear the responsibility of steering the conversation, ensuring robust participation from their peers; Empowerment Tools: To bolster their facilitation, the instructor provides these students with tools and recommended strategies beforehand; Instructor's Role: Initially, the instructor adopts a passive stance, abstaining from any direct participation for the first 15-30 minutes of the discussion, allowing students to steer the discourse; and Post-Discussion Self-Assessment: After the discussion concludes, students engage in reflective self-assessment, evaluating their degree of participation. This paper unpacks how the process of student-led discussions, framed through an autoethnographic perspective, not only fosters interpersonal competence but also amplifies the pedagogical understanding of student-driven learning experiences. Findings Through the autoethnographic lens used in this research, it becomes evident that student-led discussions not only foster a deeper engagement with assigned content but also pave the way for transformative learning experiences. These discussions serve as a forum, challenging students to grapple with cognitive dissonance and gives them the space to witness and participate in diverse communication strategies. Moreover, in allowing students to shape the narratives they perceive as most pertinent, instructors catalyze the development of profound problem-solving capabilities and a sense of agency over their learning trajectories. Key insights gleaned include the following: Collaborative learning: The discussions nurture an environment where students reflect, analyze and learn collectively, deepening their grasp of the material; Empowerment through communication: Engaging in these sessions equips learners with the prowess to reconcile disparate viewpoints, thereby fortifying their collaborative and communicative proficiencies; and Critical thinking and sustainability: By delving into intricate sustainability challenges, students hone their critical thinking capabilities, preparing them to be future custodians of a more sustainable world. In essence, when viewed through an autoethnographic lens, the student-led discussion approach not only enriches learning outcomes but also accentuates the acquisition of pivotal interpersonal competencies within the framework of HESD. Originality/value From an autoethnographic perspective, this research stands distinctively within the literature on higher education for sustainable development. While the pedagogical strategy of student-led discussions is not entirely novel, the unique application and deep introspection of this method within the realm of HESD indeed carve out new terrain. By interweaving lived experiences and educational theory, this paper offers fresh insights into how student-led discussions can be an effective way to cultivate key sustainability competencies in higher education. As such, it provides educators, scholars and practitioners a valuable reference point for fostering interpersonal skills and nuanced understandings crucial for sustainable development.

Gamification and problem-based learning (PBL): Development of creativity in the teaching-learning process of mathematics in university students

This study aims to determine whether the combination of techniques such as gamification along with problem-based learning (PBL) can significantly enhance the problem-solving abilities of university students and their academic performance compared to those who do not receive this educational approach. An experimental design was employed, incorporating the administration of tests, data collection, non-participant observation directed at teachers, and a survey administered to students. The study was conducted with 42 students in the treatment group and 37 students in the control group. As a result of the analysis, it was identified that the combination of gamification and PBL significantly improves the problem-solving capabilities of the treatment group, as well as the average grades. This suggests that these techniques can increase the engagement, motivation, and academic performance of university students in the context of mathematics. This holds significant implications for the design of educational programs and underscores the importance of integrating innovative approaches in teaching to foster holistic student development. It is recommended that educational programs and teachers consider the implementation of techniques that promote active student participation and the integration of technological resources to enhance the teaching and learning process of mathematics in university students.

PENERAPAN MODEL PEMBELAJARAN CREATIVE PROBLEM SOLVING (CPS) UNTUK MENINGKATKAN KEMAMPUAN PEMECAHAN MASALAH MATEMATIS DAN SELF-EFFICACY MATEMATIS SISWA

This research is motivated by a problem, namely the low ability to solve mathematical and self-efficacy mathematical problems where the learning process is still centered on the teacher. Thus given a solution using the Creative Problem Solving (CPS) learning model aims to determine the improvement of mathematical problem solving capabilities and mathematical self-efficacy students using the Creative Problem Solving (CPS) learning model is better than students taught with conventional learning models. The research approach is in the form of quantitative. The type of research used is Quasi experiment with non-equivalent Control Group Design research design. The population in the study was all students of class VII MTsS Jabal Nur, while the sample was class VII-1 as the experimental class and class VII-2 as the control class chosen with purposive sampling technique. The data collection techniques used are instruments for the ability to solve mathematical problems and non self-efficacy mathematical questionnaire tests are divided into pretest and post-test. Data is processed using SPSS software version 18. Analysis of data conducted for the mathematical problem solving capability test using non-parametric tests (Mann Whitney U) because there is data that is not normally distributed. Asymp sig value, (2-tailed) is 0,000 <0.05, then is rejected and is accepted. Then for the analysis of students' mathematical self-efficacy questionnaires using the normality test, homogeneity. After the data is declared normal and homogeneous Furthermore, a hypothesis test with a significant value ˂ 0.05 is 0,000, then is rejected and is accepted. It can be concluded that the increase in mathematical problem solving capabilities and mathematical self-efficacy uses the Creative Problem Solving (CPS) learning model is better than students taught with conventional learning models in class VII MTsS Jabal Nur.

Motivational factors for dietary intake behavior in gestational diabetes mellitus: A cross-sectional study

ContextDecision-making behavior pertains to the cognitive process where a patient evaluates the advantages and disadvantages of health-related decisions, taking into account their personal preferences, values, and cognitive factors. This behavior is central to the successful implementation of clinical care. Effective decision-making enhances an individual's or organization's response to challenges and opportunities, improves problem-solving capabilities, reduces risks and uncertainties, and facilitates the attainment of desired outcomes. ObjectiveThe goal of this study was to investigate the current status of dietary choice decision-making in gestational diabetes mellitus and to analyze its influencing factors, and then to find the determining factors and give targeted nursing interventions in order to improve the dietary decision-making ability of gestational diabetes mellitus patients and further standardize their dietary patterns. MethodsA cross-sectional research design was employed in this study. Convenience sampling was utilized to survey 539 GDM patients attending the obstetrics outpatient clinic and obstetrics ward of a tertiary hospital in Wuxi City, China, from March 2023 to July 2023. The survey instruments used were the General Information Questionnaire, the Motivation to Protect Pregnant Women with Gestational Diabetes Mellitus Dietary Intake Questionnaire, and the Conflict in Decision-Making Scale. ResultsThe findings were derived from data collected from 539 GDM cases. The participants' ages ranged from 19 to 52 years, with a mean age of 31.53 ± 4.37 years. The scores for GDM disease perceived susceptibility were 15.200 ± 3.481, disease perceived severity 18.455 ± 4.670, internal reward 13.226 ± 4.275, external reward 8.278 ± 2.923, response efficacy 15.078 ± 3.889, self-efficacy 18.952 ± 4.800, cost of response 14.540 ± 5.227, and decision conflict questionnaire score 70.96 ± 11.78. ConclusionsThe study revealed that GDM patients exhibited a moderate level of decision-making dilemma. Notably, the patients' perceived susceptibility and severity of the disease, along with their response efficacy and self-efficacy, positively influenced their dietary decision-making abilities. Specifically, enhancing patients' awareness of their condition and boosting their self-efficacy significantly improved their decision-making capabilities. Conversely, internal and external rewards, as well as the cost of response, exerted a negative impact on GDM patients' decision-making abilities. Therefore, it is imperative to mitigate potential barriers in GDM patients' dietary intake behaviors, thereby enhancing their decision-making skills related to dietary intake.