This study investigates the application of numerical simulations to optimize the design and operational performance of CNC machining centers, with a focus on enhancing their structural integrity and durability. The primary objective is to identify design modifications that can mitigate the risks associated with mechanical impacts and extend the service life of the machines. Finite Element Method (FEM) simulations are conducted on actual CNC machines to examine their structural responses under a range of real-world impact scenarios. The simulations reveal critical stress concentrations and deformation patterns that occur in operational environments, providing valuable insights into the dynamic behavior of the machines. A system engineering approach is employed to simplify the analysis of the machine's response to these dynamic conditions, allowing for an efficient evaluation of potential design improvements. Linear static analyses, incorporating imposed deformation conditions, are used to gain a deeper understanding of the machine’s structural weaknesses. Several model simplifications are introduced, including modifications to geometry, contact conditions, and material properties, ensuring that the quality and accuracy of the numerical models are maintained. The results highlight the potential for targeted design modifications to reduce the likelihood of mechanical failure and enhance operational efficiency. These findings suggest that the application of advanced computational mechanics can substantially improve machine performance, ultimately contributing to the longevity and reliability of CNC machining centers.
Multi-functional public teaching buildings, as high-density spaces, are subject to significant fire risks due to the large number of occupants and the complex nature of their design. In the event of a fire, the consequences can be catastrophic. Therefore, fire risk assessment is of paramount importance in the design and operation of such buildings. A comprehensive evaluation framework is proposed, integrating the Work Breakdown Structure (WBS) and the Risk Breakdown Structure (RBS) into a unified approach, referred to as the Integrated Work Breakdown Structure and Risk Breakdown Structure (i-WRBS) method. This framework identifies 15 key fire risk factors relevant to public school buildings. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to analyze the interrelationships among these factors, while PyroSim fire simulation software is used to model the dynamics of fire smoke propagation under varying wind conditions. The diffusion of smoke in stairwells is simulated under different wind speeds and directions, and the fire risk is evaluated based on the resulting outcomes. The findings indicate that both wind speed and direction play a crucial role in determining the trajectory and velocity of smoke spread, especially within stairwells. Under low wind conditions or in the absence of wind, smoke diffusion is confined to areas close to the fire source, with stairwells located farther from the fire exhibiting comparatively lower risks. However, under higher wind speeds, the speed and range of smoke diffusion are significantly increased, with a pronounced effect in the downwind direction. The fire hazards on higher floors are found to be more sensitive to variations in wind speed, as increased wind velocity leads to more substantial fluctuations in temperature caused by the combustion process. These fluctuations are exacerbated on higher floors. The findings offer valuable insights into fire risk management, contributing to the development of fire safety strategies and the formulation of evacuation plans for large public buildings.
In the context of today’s rapidly evolving automotive market, improving the reliability and efficiency of manufacturing processes remains a critical challenge for industry players. This study introduces a hybrid multi-attribute decision-making model that integrates Failure Mode and Effects Analysis (FMEA) with interval type-2 fuzzy set theory to classify and prioritize process failures. The approach enables the FMEA team to systematically identify and rank failure modes, facilitating the timely implementation of corrective actions aimed at enhancing process reliability. A key feature of the proposed model is the utilization of interval type-2 triangular fuzzy numbers (IT2TFNs), which capture the inherent uncertainty in expert assessments of risk factors (RFs). These fuzzy values are aggregated using the fuzzy harmonic mean, and the total relation matrix is derived by applying fuzzy algebraic operations, followed by defuzzification and distance calculations between fuzzy numbers. The modified Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to determine the relative weights of identified RFs, while the Multi-Attributive Border Approximation Area Comparison (MABAC) technique is used to rank failure modes based on their impact on manufacturing process reliability. The model’s effectiveness is demonstrated through its application to real-world data from an automotive supply chain, highlighting its superior capability compared to conventional approaches. This research contributes to the advancement of failure management strategies, providing a comprehensive and robust framework for decision-making in complex manufacturing environments.
The transition to renewable energy sources (RES) for electricity generation has gained significant momentum due to environmental and sustainability concerns. However, the high initial costs associated with RES implementation remain a critical barrier, particularly for micro, small, and medium enterprises (MSMEs). To address this challenge, a cost-effective optimization framework for the hybrid renewable energy system (HRES) was proposed, integrating advanced decision-making methodologies. The study focused on a case study of an MSME in a rural village in Ludhiana, Punjab, where the feasibility of various HRES configurations was evaluated using HOMER Pro software. The optimization process aims to minimize key financial metrics, including net present cost (NPC), operation and maintenance (O&M) costs, and the levelized cost of energy (LCOE), while simultaneously reducing carbon emissions. Sensitivity analyses were conducted to assess the impact of critical parameters such as diesel prices, inflation rates, and system constraints. To rank the HRES configurations, a multi-criteria decision-making (MCDM) approach is employed, combining the Method based on the Removal Effects of Criteria (MEREC) for weight determination and the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) for system ranking. The results demonstrate that the proposed framework effectively identifies the most cost-effective and environmentally sustainable HRES configuration, providing a robust decision-making tool for MSMEs. This study not only contributes to the growing body of knowledge on RES optimization but also offers practical insights for policymakers and stakeholders aiming to promote renewable energy adoption in small-scale industrial settings.
This paper investigates the kinematic solution of cable-driven hyper-redundant manipulators, focusing on the transformation from the cable-driven space to the joint space. Two forward kinematics solution networks based on residual networks and bidirectional long short-term memory (BiLSTM) networks are proposed and compared. First, a single-joint kinematic model is established based on the topology of the cable-driven hyper-redundant manipulator, providing the mapping relationship between cable length variations and joint angles. The decoupling problem between the cable-driven space and joint space is analyzed, extending the decoupling method from a two-joint scenario to a multi-joint scenario, leading to the derivation of coupled equations between cable lengths and joint angles. Subsequently, both a single-joint forward kinematics solution network and a multi-joint forward kinematics solution network are designed and trained separately. Finally, their performance is evaluated using a test dataset. The results demonstrate that the multi-joint forward kinematics solution network significantly outperforms the single-joint network in terms of both accuracy and computational efficiency.
Stance, a critical discourse marker, reflects the expression of attitudes, feelings, evaluations, or judgments by speakers or writers toward a topic or other participants in a conversation. This study investigates the manifestation of stance in the discourse of four prominent artificial intelligence (AI) chatbots—ChatGPT, Gemini, MetaAI, and Bing Copilot—focusing on three dimensions: interpersonal stance (how chatbots perceive one another), epistemic stance (their relationship to the topic of discussion), and style stance (their communicative style). Through a systematic analysis, it is revealed that these chatbots employ various stance markers, including hedging, self-mention, power dominance, alignment, and face-saving strategies. Notably, the use of face-saving framing by AI models, despite their lack of a genuine “face,” highlights the distinction between authentic interactional intent and the reproduction of linguistic conventions. This suggests that stance in AI discourse is not a product of subjective intent but rather an inherent feature of natural language. However, this study extends the discourse by examining stance as a feature of chatbot-to-chatbot communication rather than human-AI interactions, thereby bridging the gap between human linguistic behaviors and AI tendencies. It is concluded that stance is not an extraneous feature of discourse but an integral and unavoidable aspect of language use, which chatbots inevitably replicate. In other words, if chatbots must use language, then pragmatic features like stance are inevitable. Ultimately, this raises a broader question: Is it even possible for a chatbot to produce language devoid of stance? The implications of this research underscore the intrinsic connection between language use and pragmatic features, suggesting that stance is an inescapable component of any linguistic output, including that of AI systems.
Indonesia’s commitment to reducing carbon emissions is reflected in its sustainable development agenda, with mangrove ecosystems playing a critical role due to their significant carbon sequestration capacity. However, extensive degradation has been observed in the Rambat Menduyung Mangrove Area, primarily driven by unregulated mining activities in the Bangka Belitung Islands Province. A socio-ecological systems approach is essential for understanding the complex interactions between ecological processes and socio-economic drivers in this region. This study aims to assess the socio-ecological conditions of the mangrove ecosystem and formulate an effective strategy for its sustainable development. A descriptive analysis was conducted to evaluate the ecological and socio-economic characteristics of the mangrove area, while Interpretative Structural Modeling (ISM) using Exsimpro software was employed to identify key strategic components for sustainable management. The findings indicate a persistent decline in mangrove coverage, leading to reduced fishery yields and economic losses for local communities. The current economic value of mangrove-related fisheries in the region is estimated at IDR 490,508,599,620 per year, while the potential economic value of carbon sequestration is approximately IDR 23,927,229,700 per year. The proposed development strategy underscores the necessity of strengthening policy enforcement, enhancing institutional capacity, and promoting community engagement. Key stakeholders, including the West Bangka Marine and Fisheries Department (DKP West Bangka), the Forest Management Unit of Rambat Menduyung (KPHP Rambat Menduyung), and the Belo Laut Village Government, must play a central role in implementing stricter conservation policies, enforcing environmental regulations, and fostering sustainable resource utilization. Through an integrated governance framework, the long-term resilience of mangrove ecosystems in West Bangka Regency can be ensured, thereby contributing to both ecological stability and economic sustainability.
The diffusion of green innovation technologies is critically influenced by policy instruments and green indicators, with significant variations observed across different countries. This study offers a comparative analysis of the policy frameworks and green indicators implemented in Poland and Thailand to support the adoption of environmentally sustainable technologies. A narrative review was conducted, drawing on secondary sources including government reports, regional studies, and an extensive range of academic literature. The mechanisms of financial incentives, such as subsidies, tax incentives, and innovation programs, are examined to understand their role in promoting eco-friendly technologies in these two nations. Specifically, financial instruments such as the EU Cohesion Fund, the National Fund Programs, and the Bio-Circular Green Economy Program in Poland, alongside Thailand’s Solar PV Rooftop Program, are explored in detail. Additionally, the regulatory frameworks influencing green innovation adoption are discussed, highlighting the distinct approaches taken by both countries to address the challenges posed by environmental sustainability. The study identifies key green indicators—such as the Green Innovation Index, technology adoption rates, and environmental impact metrics—and compares their performance in Poland and Thailand. These indicators provide insight into the effectiveness of policy instruments in achieving green innovation goals. The findings suggest that while both countries have made considerable strides in fostering green innovation, the outcomes are influenced by unique socio-economic and environmental contexts. It is recommended that policymakers adopt tailored, comprehensive frameworks, incorporating robust green indicators, to guide future efforts in green innovation diffusion. This study underscores the need for context-specific policy interventions to accelerate the transition to a green economy.
This study examines the influence of identity politics on climate policy over the period 2003–2024 through a bibliometric analysis, with a specific focus on the interconnections between climate change, policy uncertainty, and identity politics. Using data extracted from the Scopus database, thematic mapping and co-occurrence analysis were conducted via Biblioshiny software to identify key research trends, thematic networks, and collaboration patterns. The findings demonstrate that identity politics has intensified public polarization regarding climate issues, obstructing global consensus-building and compromising the sustainability of climate policy frameworks. The analysis reveals that political affiliations significantly shape public perceptions and support for climate initiatives, contributing to policy uncertainty and constraining investments in green technologies. Furthermore, the role of identity politics in framing national and international climate discourses is highlighted, illustrating its capacity to hinder cooperation across diverse stakeholder groups. The study emphasizes the critical need for inclusive and depolarized approaches to climate policy formulation, which are essential for overcoming the challenges posed by political polarization. By addressing gaps in the literature and providing an overview of existing collaboration networks, this research offers valuable insights into the nexus of identity politics and climate action. Key recommendations are proposed to foster more sustainable and equitable climate strategies, ensuring that political and social divides are effectively addressed to enable long-term climate resilience.
The extent to which students’ cultural values influence mathematical problem-solving skills was investigated, with emphasis placed on the moderating effect of prior mathematical knowledge and the mediating role of student motivation. A mixed-methods design was employed to ensure both quantitative and qualitative dimensions of the inquiry were addressed, enabling a comprehensive understanding of the underlying relationships. A purposive sample of 370 students from culturally diverse regions of Ghana was selected to ensure contextual validity and sociocultural relevance. It was found that students’ cultural values significantly shaped their problem-solving performance, particularly in relation to cognitive processing strategies and the selection of problem-solving heuristics. The relationship between cultural values and problem-solving skills was moderated by students’ prior knowledge, with students possessing a stronger foundational understanding of mathematics deriving greater benefit from cultural alignment. Contrary to expectations, the mediating effect of student motivation on this relationship was not supported, suggesting that while motivation is influenced by cultural values, it does not serve as a direct conduit for enhanced problem-solving capability. These findings underscore the necessity of incorporating culturally responsive pedagogical strategies that recognize and harness cultural value systems as cognitive assets. Furthermore, implications for curriculum development and teacher training were discussed, with a recommendation that future research explore context-specific interventions that operationalize cultural capital to improve mathematical learning trajectories.
Open Access
