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The objective of this research is to develop a model for life skills training through the strengthening of academic, personal, social, and career competencies for middle school students. We designed this model to equip students with the necessary skills to navigate the complexities of life's challenges. Design-Based Research (DBR) serves as a research methodology, encompassing four stages: identification and analysis of practical problems, solution design, iterative testing cycles and solution refinement, reflection, and producing the final design. Validation testing of the model was conducted with four experts, including one developmental guidance and counseling specialist, one developmental psychologist, one adolescent psychologist, one curriculum and learning specialist, and eight guidance and counseling teachers or practitioners. The results of expert and practitioner validation indicate that the life skills training model is worthy of further experimentation to develop student's life skills. Recommendations for future research emphasize the importance of developing new skills or competencies for the younger generation and educators to address the challenges and dynamics of the digital era. Globalization and societal shifts toward digitally automated services underscore the need for teacher preparation in imparting life skills that not only adhere to conventional norms but also integrate technological skills into student education.

Abstract Given the increasing importance of liquefied natural gas in the global energy system, an understanding of natural gas value chain Greenhouse gas emissions is increasingly important. Here, we develop models of the key unit processes in the liquefied natural gas value chain: liquefaction, shipping, and regasification. We utilize detailed process-level data for liquefaction terminals and ships to generate granular estimates. Average gate-to-gate emissions of 0.220 and 0.269 tonnes of CO2e/tonne gas liquefied were estimated for US liquefied gas exported from Sabine Pass and Cove Point liquefaction terminals, respectively. Furthermore, 331 voyages are modeled from four US ports reaching 30 countries, and the average gate-to-gate emissions for the countries to Asia are the highest at 0.200 tonnes CO2e/tonnes of gas delivered. The estimated average gate-to-gate emissions for regasification terminals is 0.021 tonnes CO2e/tonne of gas regasified. The developed models can be expanded to other regions, offering a global analysis provision.

This research aims to develop the product of the life skill education program (LSEP) which is accurate, credible, and effective. This research used the Plomp model. The model covers the input, process, output, outcome and consists of instrument, scoring guidance, and good or bad criteria. The instruments used in the model are the questionnaire, observation sheet and interview guide. The content validity of the questionnaire and observation sheet was proved by expert judgement and continued by using the Aiken Formula, the construct validity of the questionnaire was proved by the construct validity using the exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The content validity of the questionnaire and observation sheet involved 9 validity experts. The questionnaire construct was done in two steps; the first step involved 65 students analyzed using EFA and the second step involved 199 students analyzed with CFA. The reliability of the observation sheet and questionnaire was estimated by using the Cronbach Alpha technique. The result of the trial model and its analysis shows that all the instruments are good. LSEP model is tested by involving 15 students in the course and training institute. The result of the trial model shows that the model is effective because the users explains that the model is: a) comprehensive, covering many components and sub component programs such as the input, process, output, outcome, b) practical, simple and easy in usage, c) economical, not needing much cost, energy, and time, also d) supported by valid and reliable data collection instrument.

Life skills refer to a person's ability to do life well through the successful completion of everyday tasks and a meaningful engagement at school, at work, at home and in the community. However, several scholars have critiqued existing approaches to life skills teaching in sport, often situated as individualistic and focused on economic productivity and integration in predetermined social orders. For the teaching of life skills in sport to instigate social change, coaching approaches must consider broader structures of power, privilege and oppression. The purpose of the paper is to propose a two continua model coalescing implicit/explicit approaches and normative/transformative dimensions of how coaches can teach life skills through sport. The model is designed to account for how coaches can be implicit or explicit in their approaches to teaching life skills, doing so in manners that can be normative or transformative. Within each of the model's quarter-circles, examples are offered as to how coaches can teach life skills in approaches considered normative implicit, normative explicit, transformative implicit and transformative explicit. In the transformative explicit quarter-circle, the concept of social justice life skills is expanded to include dignity, reciprocity, transformative leadership and critical consciousness. Implications for research and practice are offered in terms of situating youth development from a relational perspective and positioning transformative change as a complex process that carries certain challenges and risks for coaches and youth. The two continua model for life skills teaching offers researchers and coaches a conceptual framework that delineates actions that can be taken to enact transformative pedagogies, while also opening new horizons for what life skills can become when envisioned with social change in mind.

ObjectivesA life cycle carbon emission assessment method is proposed to comprehensively evaluate the carbon emission reduction capacity of hydrogen-electric hybrid ships. MethodsFirst, an energy flow model of the power system is established using the Matlab/Simulink software platform. Hydrogen consumption and electricity consumption under actual operating conditions are then calculated, and the life cycle carbon emissions are obtained. Finally, sensitivity analysis is performed on carbon emissions from different power sources and hydrogen sources.ResultsThe results of an inland river ferry show that compared with the traditional diesel engine power system, the hydrogen-electric hybrid system can reduce carbon emissions by 30.24% in one operation cycle. The sensitivity analysis results show that the carbon emissions of the hydrogen-electric hybrid system are not necessarily better than those of the traditional diesel power system, so it is recommended to use renewable energy for hydrogen production and power generation to significantly reduce carbon emissions (up to 94.2%).ConclusionsThe results of this study can provide references for the power system design of green ships.

ObjectiveLife satisfaction pertains to an individual’s subjective evaluation of their life quality, grounded in their personal criteria. It stands as a crucial cognitive aspect of subjective wellbeing, offering a reliable gauge of a person’s comprehensive wellbeing status. In this research, our objective is to develop a hybrid self-supervised model tailored for predicting individuals’ life satisfaction in South Korea.MethodsWe employed the Busan Metropolitan City Social Survey Data in 2021, a comprehensive dataset compiled by the Big Data Statistics Division of Busan Metropolitan City. After preprocessing, our analysis focused on a total of 32,390 individuals with 51 variables. We developed the self-supervised pre-training TabNet model as a key component of this study. In addition, we integrated the proposed model with the Local Interpretable Model-agnostic Explanation (LIME) technique to enhance the ease and intuitiveness of interpreting local model behavior.ResultsThe performance of our advanced model surpassed conventional tree-based ML models, registering an AUC of 0.7778 for the training set and 0.7757 for the test set. Furthermore, our integrated model simplifies and clarifies the interpretation of local model actions, effectively navigating past the intricate nuances of TabNet’s standard explanatory mechanisms.ConclusionOur proposed model offers a transparent understanding of AI decisions, making it a valuable tool for professionals in the social sciences and psychology, even if they lack expertise in data analytics.

Life-planning competence is an integral personality trait that characterizes one’s desire and ability to implement one’s potential, i.e., knowledge, skills, experience, character traits, etc., in order to achieve one’s life objectives. It consists of motivational, cognitive, emotional, volitional, axiological, semantic, operational, reflective, and proactive components. The present research featured the life-planning competence in high school students (n = 200; 2019–2022) of the city of Krasnoyarsk, Russia. The resulting strategies demonstrated a connection with the concept of being-inthe-world by M. Heidegger and L. Binswanger, as well as A. Längle’s concept of fundamental existential motivations. The statistical analysis involved the principal component method with Varimax rotation and Kaiser sampling. The factor analysis revealed eight factors: 1) purposefulness, meaningfulness, and readiness for life planning; 2) cooperation; 3) reflexive activity control; 4) correlation with the world; 5) control of oneself and the situation; 6) self-awareness; 7) dynamic balance; 8) self-development. These factors connected the process of life-planning with the four existential fundamental motivations identified by A. Längle and the forms of being-in-the-world described by M. Heidegger and L. Binswanger. The high school students demonstrated a motivation for physical survival and spiritual overcoming of existence, as well as a desire to enjoy life, experience values, find self-identity, and seek justice. As teenagers, they lacked the fourth existential motivation, i.e., motivation to obtain existential meaning.

Modernity is characterised by a specific scientific paradigm based on binary logic. Neither freedom nor creativity have a place in this logical model. On the contrary, triadic logic enables rational consideration of an anthropological and social model compatible with both things: freedom and creativity, and therefore with Christianity. Furthermore, triadic logic provides us with a model of thought compatible with the current physical explanation of reality.

Objectives The presented article is theoretical and analytical in nature. Its main purpose is to model how to build employee satisfaction in organizations focused on developing talent among employees through implemented talent management programs. Material and methods The basis for writing the article was the results of our own research, carried out 2022, using the CAWI method on a representative sample of 280 companies with talent development policies. The main objective of the survey was to determine what strategies Polish companies adopt in terms of talent development in their organizations and what impact they have on employee satisfaction. Results The results indicate the complex course of the process of building employee satisfaction, in which life satisfaction and job satisfaction should be taken into account. Areas significantly affecting the life satisfaction of an employee of a talent management organization are job satisfaction, work environment and development areas available to the employee. Conclusions On the other hand, work environment and development areas available to the employee are important for achieving job satisfaction.

This paper presents a novel decision-making framework for the life cycle management of aircraft components, integrating advanced data analytics, artificial intelligence, and predictive maintenance strategies. The proposed model addresses the challenges of balancing safety, reliability, and cost-effectiveness in aircraft maintenance. By using real-time health monitoring systems, failure probability models, and economic analysis, the framework enables more informed and dynamic maintenance strategies. The model incorporates a comprehensive approach that combines reliability assessment, economic analysis, and continuous re-evaluation to optimize maintenance, replacement, and life extension decisions. The optimization method on the base of genetic algorithm (GA) is employed to minimize total life cycle costs while maintaining component reliability within acceptable thresholds. The framework’s effectiveness is demonstrated through case studies on three distinct aircraft components: mechanical, avionics, and engine. These studies showcase the model’s versatility in handling different failure patterns and maintenance requirements. This study introduces a data-driven decision-making framework for optimizing the life cycle management of aircraft components, focusing on reliability, cost-effectiveness, and safety. To achieve optimal maintenance scheduling and resource allocation, a GA is employed, allowing for an effective exploration of complex solution spaces and enabling dynamic decision-making based on real-time data inputs. The GA-based optimization approach minimizes total life cycle costs while maintaining component reliability, with the framework’s effectiveness demonstrated through case studies on key aircraft components. Key findings from the case study demonstrate significant cost reductions through optimization, with mechanical components showing a 10% more reduction in total life cycle costs, avionics components achieving a 14% more cost reduction, and engine components demonstrating a 7% more decrease in total costs. The research also presents an optimized dynamic maintenance schedule that adapts to real-time component health data, extending component lifespans and reducing unexpected failures. The framework effectively addresses key industry challenges such as no fault found events while minimizing unexpected failures and enhancing the overall reliability and safety of aircraft maintenance practices. Sensitivity analysis further demonstrates the model’s robustness, showing stable performance under varying failure rates, maintenance costs, and degradation rates. The study contributes a scalable approach to predictive maintenance, balancing safety, cost, and resource allocation in dynamic operational environments.

Motivation analysis is significant in inspecting illegal power consumption behavior. Based on enterprise life cycle theory, this study proposes enterprise electricity consumption life cycle to conceptually aid the analysis of motives for enterprise illegal power consumption behavior. The identification of the aforementioned life cycle is achieved by clustering. We propose a deep clustering model based on the joint optimization of Gated Recurrent Unit (GRU) autoencoder and an improved K-prototypes algorithm. The proposed model utilizes GRU autoencoder to learn the latent features of high-dimensional enterprise electricity data and dynamically adjust the distance metric in K-prototypes algorithm based on the reconstruction error of GRU autoencoder. By jointly optimizing the two components, the proposed model can effectively cluster the enterprise electricity consumers and enterprises’ electricity consumption life cycle is identified based on the features and patterns of enterprises in each cluster. Experiment demonstrates that the proposed model can effectively capture the non-linear transformation from electricity data to its latent space and yield superior clustering results, and can provide data support for the identification of enterprise electricity consumption life cycle.

Sustainable waste management strategies are urgently needed due to an increasing global population and increased waste production. In this context, biorefineries have recently emerged as a promising approach to valorize waste streams and supply a broad range of products. This study presents the process model and life cycle assessment (LCA) of a biorefinery concept using a novel biochemical method, a so-called "dark photosynthesis" conversion. This process is coupled to a photo-fermentation using microalgae. Overall, the biorefinery concept can produce hydrogen, lutein, β-carotene, and proteins for animal feed. Apple pomace from apple juice production is used as feedstock for the primary conversion step. A process model was created with the process simulation software Aspen Plus® using experimental and literature data. Results from this model were then used in an LCA. The environmental impacts of the proposed biorefinery concept are relatively high, showing the need for process optimization in several areas. Energy system integration, stream recycling, and higher hydrogen yields are recognized as especially important for improving the environmental performance of this concept. Despite these findings, the model shows the feasibility of implementing the biochemical conversion technologies in a biorefinery concept for effectively utilizing residue streams. [ABSTRACT FROM AUTHOR]

Life skills as skills needed to overcome the demands and demands of everyday life to see an increase in life skills in research students used two learning models, namely the Teaching Personal and Social Responsibility (TPSR) model and the cooperative model. The purpose of this study was to find out how much influence the Teaching Personal and Social Responsibility (TPSR) model and cooperative model had on life skills in physical education, sports and health. The research method in this study is to use a comparative experimental method, comparative research to find out whether two variables or more than two groups have differences in the aspects or variables studied. The experimental research design used in this study was the Pretest-Posttest Control Group Design. The samples used in this study were 15 students in the TPSR group and 15 students in the cooperative group. With the application of the TPSR learning model which aims to improve aspects of students' life skills, the average pretest score = 150.13 and the posttest average score = 166.40 with a difference in score of 16.27. As for the cooperative learning model which aims to improve aspects of students' life skills, the average pretest score = 112.27 and the posttest average score = 158.33 with a difference in score of 46.07. Thus, the difference in the average value of the TPSR learning model is smaller than the difference in the average value of the cooperative learning model, so that it can be concluded that the cooperative learning model has more influence on students' life skills.

BackgroundUnderstanding stand dynamics is essential for predicting future wood supply and associated ecosystem services for sustainable forest management. The dynamics of natural stands can be characterized by age-dependent growth and yield models. However, dynamics in managed stands appear somewhat different from that of natural stands, especially with difficulties in explaining the phenomenon of post-thinning overcompensation, based upon some long-term observations. Though overcompensation is an ideal outcome for the forest sector, it had been largely treated as an outlier and thus ignored or dismissed as “out-of-the-ordinary”.MethodologyWe developed a life history theory-based, state-dependent model of Tree Adaptive Growth (TAG) to investigate this phenomenon and verified that overcompensation should be a common outcome in post-thinning forest stands when the stand growth over time is sigmoid shaped. TAG posits that individual trees will invest proportionately more into growth following thinning because it is evolutionarily adaptive to do so.ResultsOur investigation of the model’s behavior unearthed diverse stand growth patterns similar to that which is observed in the empirical datasets and predicted by a statistics-based Tree’s Compensatory Growth (TreeCG) model.ConclusionA simple, theory-driven, analytical model, TAG, can reproduce the diverse growth patterns in post-thinning stands and thus assist addressing silviculture-related issues. The model can be applied to various jurisdictions even without detailed regional growth and yield relationships and is capable of incorporating the effects of other time sensitive factors like fertilization, pruning, and climate change.