Search Results (60)

The system analysis method is suitable for detecting the optimal pathways for regional sustainable (e.g., green, low carbon) socioeconomic development. This study develops an inexact fractional energy–output–water–carbon nexus system planning model to minimize total carbon emission intensity (CEI, total carbon emissions/total economic output) under a set of nexus constraints. Superior to related research, the model (i) proposes a CEI considering both sectoral intermediate use (indirect) and final use (direct); (ii) quantifies the dependencies among energy, output, water, and carbon; (iii) restricts water utilization for carbon emission mitigation; (iv) adopts diverse mitigation measures to achieve carbon neutrality; (v) handles correlative chance-constraints and crisp credibility-constraints. A case in Fujian province (in China) is conducted to verify its feasibility. Results disclose that the total CEI would fluctuate between 45.05 g/CNY and 47.67 g/CNY under uncertainties. The annual total energy and total output would, on average, increase by 0.58% and 2.82%, respectively. Eight mitigation measures would be adopted to reduce the final carbon emission into the air to 0 by 2060. Compared with 2025, using water for carbon emission mitigation would increase 17-fold by 2060. For inland regions, authorities should incorporate other unconventional water sources. In addition, the coefficients of embodied energy consumption and water utilization are the most critical parameters. Full article

The heavy environmental impact of the construction industry—responsible for 39% of world CO₂ emissions and consuming over 40% of natural resources—supports the need for evidence-based decision-making tools for sustainable material selection balancing environmental, economic, and social considerations. This research develops and evaluates an integrated decision support system that couples cradle-to-grave lifecycle assessment (LCA) with various multi-criteria decision-making (MCDM) methods to optimize climate-resilient material selection for schools. The methodology is an integration of hybrid Analytic Hierarchy Process–Technique for Order of Preference by Similarity to Ideal Solution (AHP-TOPSIS) and VIKOR techniques validated with eight case studies in hot-arid, hot-humid, and temperate climates. Environmental, economic, social, and technical performance indices were evaluated from primary experimental data and with the input from 22 international experts with climate change assessment expertise. Ten material options were examined, from traditional, recycled, and bio-based to advanced composite systems throughout full building lifecycles. The results indicate geopolymer–biofiber composite systems achieve 42% reduced lifecycle carbon emissions, 28% lower cost of ownership, and 35% improved overall sustainability performance compared to traditional equivalents. Three MCDM techniques’ cross-validation demonstrated a satisfactory ranking correlation (Kendall’s τ = 0.87), while Monte Carlo uncertainty analysis ensured framework stability across 95% confidence ranges. Climate-adaptive weighting detected dramatic regional optimization contrasts: thermal performance maximization in tropical climates and embodied impact emphasis in temperate climates. Three case studies on educational building projects demonstrated 95.8% accuracy in validation of environmental performance and economic payback periods between 4.2 and 6.8 years in real-world practice. Full article

We build on the predictive processing framework to show that intelligent behaviour is adaptive control, driven by accurate prediction and uncertainty reduction in dynamic environments with limited information. We argue that adaptive control arises through a process of re-concretisation, where learned abstractions are grounded in new situations via embodiment. We use this as an explanation of why AI models often generalise at the cost of detail while biological systems manage to tailor their predictions towards specific environments over time. On this basis, we utilise the notion of embodied prediction to provide a new distinction between biological intelligence and the performance illustrated by AI systems. Full article

Predictive Coding (PC) frameworks claim to model recognition via prediction–error loops, but they often lack explicit biological implementation of fast familiar recognition and impose latency that limits real-time robotic control. We begin with Experience-Based Pattern Matching (EBPM), a biologically grounded mechanism inspired by neural engram reactivation, enabling near-instantaneous recognition of familiar stimuli without iterative inference. Building upon this, we propose Dynamic Integrative Matching and Encoding (DIME), a hybrid system that relies on EBPM under familiar and low-uncertainty conditions and dynamically engages PC when confronted with novelty or high uncertainty. We evaluate EBPM, PC, and DIME across multiple image datasets (MNIST, Fashion-MNIST, CIFAR-10) and on a robotic obstacle-course simulation. Results from multi-seed experiments with ablation and complexity analyses show that EBPM achieves minimal latency (e.g., ~0.03 ms/ex in MNIST, ~0.026 ms/step in robotics) but poor performance in novel or noisy cases; PC exhibits robustness at a high cost; DIME delivers strong trade-offs—boosted accuracy in familiar clean situations (+4–5% over EBPM on CIFAR-10), while cutting PC invocations by ~50% relative to pure PC. Our contributions: (i) formalizing EBPM as a neurocomputational algorithm built from biologically plausible principles, (ii) developing DIME as a dynamic EBPM–PC integrator, (iii) providing ablation and complexity analyses illuminating component roles, and (iv) offering empirical validation in both perceptual and embodied robotic scenarios—paving the way for low-latency recognition systems. Full article

This paper provides a theoretical investigation of the OR-product (∨-product) in soft set theory, an operation of central importance for handling uncertainty in decision-making. A comprehensive algebraic analysis is carried out with respect to various types of subsets and equalities, with particular emphasis on M-subset and M-equality, which represent the strictest forms of subsethood and equality. This framework reveals intrinsic algebraic symmetries, particularly in commutativity, associativity, and idempotency, which enrich the structural understanding of soft set theory. In addition, certain missing results on OR-products in the literature are completed, and our findings are systematically compared with existing ones, ensuring a more rigorous theoretical framework. A central contribution of this study is the demonstration that the collection of all soft sets over a universe, equipped with a restricted/extended intersection and the OR-product, forms a commutative hemiring with identity under soft L-equality. This structural result situates the OR-product within one of the most fundamental algebraic frameworks, connecting soft set theory with broader areas of algebra. To illustrate its practical relevance, the int-uni decision-making method on the OR-product is applied to a pilot recruitment case, showing how theoretical insights can support fair and transparent multi-criteria decision-making under uncertainty. From an applied perspective, these findings embody a form of symmetry in decision-making, ensuring fairness and balanced evaluation among multiple decision-makers. By bridging abstract algebraic development with concrete decision-making applications, the results affirm the dual significance of the OR-product—strengthening the theoretical framework of soft set theory while also providing a viable methodology for applied decision-making contexts. Full article

Despite increased interest in embodied learning, the role of sensorimotor activity in shaping students’ probabilistic reasoning remains underexplored. This design-based study examines how high school students develop key probabilistic concepts, including sample space, certainty, and event probability, through whole-body movement activities situated in an authentic classroom setting. Grounded in embodied cognition theory, we introduce a two-axis interpretive framework. One axis spans sensorimotor exploration and formal reasoning, drawing from established continuums in the literature. The second axis, derived inductively from our analysis, contrasts engagement with distraction, foregrounding the affective and attentional dimensions of embodied participation. Students engaged in structured yet open-ended movement sequences that elicited intuitive insights. This approach, epitomized by one student’s spontaneous question, “Can I use my leg too?”, captures the agentive and improvisational character of the embodied learning environment. Through five analyzed classroom episodes, we trace how students shifted between bodily exploration and formalization, often through nonlinear trajectories shaped by play, uncertainty, and emotionally driven reflection. While moments of insight emerged organically, they were also fragile, as they were affected by ambiguity and the difficulty in translating physical actions into mathematical language. Our findings underscore the pedagogical potential of embodied design for probabilistic learning while also highlighting the need for responsive teaching that balances structure with improvisation and supports affective integration throughout the learning process. Full article

Against the background of the unprecedented advancements related to Industry 4.0 and beyond, transitioning from classical mathematical models to fully embodied digital twins represents a critical change in the planning, monitoring, and optimization of complex industrial systems. This work outlines the subject within the broader field of applied mathematics and computational simulation while highlighting the critical role of sound mathematical foundations, numerical methodologies, and advanced computational tools in creating data-informed virtual models of physical infrastructures and processes in real time. The discussion includes examples related to smart manufacturing, additive manufacturing technologies, and cyber–physical systems with a focus on the potential for collaboration between physics-informed simulations, data unification, and hybrid machine learning approaches. Central issues including a lack of scalability, measuring uncertainties, interoperability challenges, and ethical concerns are discussed along with rising opportunities for multi/macrodisciplinary research and innovation. This work argues in favor of the continued integration of advanced mathematical approaches with state-of-the-art technologies including artificial intelligence, edge computing, and fifth-generation communication networks with a focus on deploying self-regulating autonomous digital twins. Finally, defeating these challenges via effective collaboration between academia and industry will provide unprecedented society- and economy-wide benefits leading to resilient, optimized, and intelligent systems that mark the future of critical industries and services. Full article

This paper presents a decentralized energy management system (EMS) based on Multilayer Perceptron Artificial Neural Networks (MLP-ANNs) and a Modified Cheetah Optimizer (MCO) to account for uncertainty in renewable generation and load demand. The proposed framework applies an MLP-ANN with Levenberg–Marquardt (LM) training for high-precision forecasts of photovoltaic/wind generation, ambient temperature, and load demand, greatly outperforming traditional statistical methods (e.g., time-series analysis) and resilient backpropagation (RP) in precision. The new MCO algorithm eliminates local trapping and premature convergence issues in classical optimization methods like Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). Simulations on a test microgrid verily demonstrate the advantages of the framework, achieving a 26.8% cost-of-operation reduction against rule-based EMSs and classical PSO/GA, and a 15% improvement in forecast accuracy using an LM-trained MLP-ANN. Moreover, demand response programs embodied in the system reduce peak loads by 7.5% further enhancing grid stability. The MLP-ANN forecasting–MCO optimization duet is an effective and cost-competitive decentralized microgrid management solution under uncertainty. Full article

This survey provides a comprehensive review of the integration of large language models (LLMs) into autonomous robotic systems, organized around four key pillars: locomotion, navigation, manipulation, and voice-based interaction. We examine how LLMs enhance robotic autonomy by translating high-level natural language commands into low-level control signals, supporting semantic planning and enabling adaptive execution. Systems like SayTap improve gait stability through LLM-generated contact patterns, while TrustNavGPT achieves a 5.7% word error rate (WER) under noisy voice-guided conditions by modeling user uncertainty. Frameworks such as MapGPT, LLM-Planner, and 3D-LOTUS++ integrate multi-modal data—including vision, speech, and proprioception—for robust planning and real-time recovery. We also highlight the use of physics-informed neural networks (PINNs) to model object deformation and support precision in contact-rich manipulation tasks. To bridge the gap between simulation and real-world deployment, we synthesize best practices from benchmark datasets (e.g., RH20T, Open X-Embodiment) and training pipelines designed for one-shot imitation learning and cross-embodiment generalization. Additionally, we analyze deployment trade-offs across cloud, edge, and hybrid architectures, emphasizing latency, scalability, and privacy. The survey concludes with a multi-dimensional taxonomy and cross-domain synthesis, offering design insights and future directions for building intelligent, human-aligned robotic systems powered by LLMs. Full article

Indonesia plays a dominant role in the global refined palm oil (RPO) supply chain. Given the increasing global emphasis on carbon neutrality and sustainable trade, understanding the carbon footprint of Indonesian RPO and its embodied carbon emissions (ECE) in global trade is essential for identifying mitigation opportunities and aligning with international sustainability standards. This study integrates life cycle assessment and trade data to quantify the carbon footprint of RPO products and analyze the spatiotemporal patterns of trade-related ECE. Results show that producing 1 ton of RPO emits 2196.84 kg CO₂e, with wastewater treatment (57.67%) and land use change (32.82%) as the main contributors. From 2010 to 2022, ECE induced by RPO exports rose from 35.79 Mt CO₂e to 54.94 Mt CO₂e (3.64% annual growth). Major ECE importers were India, China, and Pakistan, accounting for 20.36%, 14.29%, and 11.45% of Indonesia’s total trade-related ECE, respectively. Comprehensive sensitivity and uncertainty analyses conducted on key parameters confirmed the robustness of the above results. Based on these robust findings, integrated mitigation strategies targeting both production optimization and sustainable trade mechanisms are proposed to accelerate Indonesia’s RPO industry decarbonization. Full article

As a knowledge-intensive activity, the Kansei engineering (KE) process encounters numerous challenges in the design knowledge flow, primarily due to issues related to information reliability, uncertainty, and subjectivity. Bridging this gap, this study introduces an advanced KE framework integrating a cloud model with Z-numbers (CMZ) and Bayesian elastic net regression (BENR). In stage-I of this KE, data mining techniques are employed to process online user reviews, coupled with a similarity analysis of affective word clusters to identify representative emotional descriptors. During stage-II, the CMZ algorithm refines K-means clustering outcomes for market-representative product forms, enabling precise feature characterization and experimental prototype development. Stage-III addresses linguistic uncertainties in affective modeling through CMZ-augmented semantic differential questionnaires, achieving a multi-granular representation of subjective evaluations. Subsequently, stage-IV employs BENR for automated hyperparameter optimization in design knowledge inference, eliminating manual intervention. The framework’s efficacy is empirically validated through a domestic cleaning robot case study, demonstrating superior performance in resolving multiple information processing challenges via comparative experiments. Results confirm that this KE framework significantly improves uncertainty management in design knowledge flow compared to conventional implementations. Furthermore, by leveraging the intrinsic symmetry of the normal cloud model with Z-numbers distributions and the balanced ℓ₁/ℓ₂ regularization of BENR, CMZ–BENR framework embodies the principle of structural harmony. Full article

The development of the digital economy is a strategic choice for seizing new opportunities in the latest wave of technological revolution and industrial transformation. As a critical tool for driving the digital transformation of enterprises, digital platforms play a pivotal role in this process. This study employs the evolutionary game theory of complex networks to develop a game model for the digital transformation of enterprises and utilizes the Fermi rule from sociophysics to characterize the evolution of enterprise strategies. Throughout this process, the interactive behaviors and strategic choices of enterprises embody the features of information flow and dynamic adjustment within the network. These features are crucial for elucidating the complexity and uncertainty inherent in strategic decision-making. The research findings indicate that digital platforms, through the provision of high-quality services and the implementation of effective pricing strategies, can significantly reduce the costs associated with digital transformation, thereby enhancing operational efficiency and innovation capacity. Moreover, the model reveals the competitive relationships between enterprises and their impact on transformation strategies, offering theoretical insights for policymakers. Based on these findings, the paper proposes policy recommendations such as strengthening infrastructure, implementing differentiated service strategies, and enhancing decision-making capability training, with the aim of supporting the digital transformation of enterprises across various industries and promoting sustainable development. Full article

The estimation of water requirements constitutes a critical prerequisite for delineating water scarcity hotspots and mitigating intersectoral competition, particularly in endorheic basins in arid or semi-arid regions where hydrological closure exacerbates resource allocation conflicts. Under conditions of water scarcity, water supplied locally by precipitation and shallow groundwater bodies should be taken into account to estimate the net water requirements to be met with water conveyed from off-site sources. This concept is embodied in the distinction of blue ET (BET) and green ET (GET). In this study, the Budyko hypothesis (BH) method was optimized to partition the total ET into GET and BET during 2001–2018 in the Heihe River Basin. In this region, a better knowledge of net water requirements is even more important due to water allocation policies which reduced water supply to irrigated lands in the last 15 years. This study proposes a modified BH method based on a new vegetation-specific parameter (${\omega }_v$) which was optimized for different vegetation types using precipitation and actual ET data obtained from remote sensing observations. The results show that the BH method partitioned GET and BET reasonably well, with a percent bias of 23.8% and 37.4% and a root mean square error of 84.8 mm/a and 113.6 mm/a, respectively, when compared with reported data, which are superior to that of the precipitation deficit and soil water balance methods. A sensitivity experiment showed that the BH method exhibits a low sensitivity to uncertainties of input data. The results documented differences in the contribution of GET and BET to total ET across different land cover types in the Heihe River Basin. As expected, rainfed forest and grassland ecosystems are predominantly governed by GET, with 81.3% and 87.2% of total ET, respectively. In contrast, croplands and shrublands are primarily regulated by BET, with contributions of 61.5% and 84.3% to total ET. The improved BH method developed in this study paves the way for further analyses of the net water requirements in arid and semi-arid regions. Full article

This paper introduces an innovative approach to optimizing flight corridors under complex constraints, particularly focusing on the Terminal Area Energy Management (TAEM) phases of reusable vehicles, where nominal trajectories may be inadequate due to initial condition and aerodynamic deviations. Leveraging the elegant principles of symmetry, the proposed optimal flight corridor design method, based on the Lagrange multiplier technique, offers a harmonious balance between trajectory accuracy and adaptability. By describing the TAEM flight corridor through a range–altitude profile and utilizing iterative optimization to uphold physical constraints such as dynamic pressure, overload, and roll angle, this method ensures symmetrical alignment of the design parameters. Through a comprehensive analysis of aerodynamic and initial position uncertainties, this method showcases exceptional symmetry in adapting to trajectory design uncertainties. The simulation results demonstrate the resilient nature of the designed flight corridor, capable of seamlessly accommodating initial state deviations and aerodynamic uncertainties. This symmetrical optimization of flight corridors not only enhances trajectory planning and control capabilities during the terminal energy management phase, but also showcases a paradigm shift towards precision and balance in aerospace engineering. Our simulation findings underscore the efficiency of this approach by reducing the flight corridor range by 50% compared to the nominal state while maintaining robustness across deviation conditions, embodying the symmetrical resilience needed for optimal trajectory design. Full article

This study evaluates the environmental impact of alkali-activated concrete materials (AACMs) as alternatives to conventional concrete. The influence of binder and activator content and type, along with other mix parameters, is analysed using a probabilistic embodied carbon assessment on a large dataset that includes 580 mixes. Using a cradle-to-gate approach with region-specific life-cycle inventory data, emissions are analysed against binder intensity, activator-to-binder and water-to-binder ratios, and fresh/mechanical properties. A multicriteria assessment quantifies the best-performing mix in terms of embodied carbon, compressive strength, and slump. AACM environmental impact is compared to conventional concrete through existing classification schemes and literature. AACM emissions vary between 41 and 261 kgCO₂eq/m³, with activators contributing the most (3–198 kgCO₂eq/m³). Uncertainty in transport-related emissions could shift these values by ±38%. AACMs can achieve up to four-fold less emissions for high-strength materials compared to conventional concrete, although this benefit decreases with lower mechanical properties. AACM environmental sustainability depends on activator characteristics, curing, mix design, and transportation. Full article