The ever-growing number of energy requirements needed in cloud data centers has led to the rising use of intelligent, sustainable and environmental scheduling algorithms. The study proposes a new hybrid algorithm that is a fusion of Proximal Policy Optimization (PPO) and Round Robin (RR), and is called PPO-RR, to enhance the efficiency of virtual machine (VM) scheduling and reduce the amount of energy consumption in a virtualized cloud system. The suggested approach follows the strategy of reinforcement learning: using the feedback about the system state, it can actively optimize the scheduling decisions to improve resource utilization and eliminate unnecessary waste of power in idle conditions.Experiments were carried using an artificially created cloud environment dataset. The PPO-RR model was seen to have shown significant performance gains over the conventional RR and heuristic scheduling techniques. To be more precise, PPO-RR achieved the accuracy of 95.3%, precision of 94.6%, recall of 95.4, and F1-score of 94.9. Such findings demonstrate the superiority of the model in its capacity to assign the VMs to the hosts appropriately during different workloads.With regard to energy measures, PPO-RR saved 1520 kWh to 1235 kWh translating to about 56.3 percent of energy saved as compared to conventions practices. The PPO-RR confusion matrix shows that the True Positives (TP) are 420, True Negatives (TN) are 360, False Positives (FP) are 15 and False Negatives (FN) are 25 which means that the accuracy of the classification of the decisions in scheduling is high.The model offers dynamic scheduling with performance and energy benefits compared to the traditional schedulers because the PPO-RR model adapts to the work load profile and system conditions in a real-time fashion. Despite the fact that this study shows PPO-RR to positively perform in various metrics, no formal statistical test of significance (e.g., t-test) was conducted and will be done in subsequent research.In sum, the PPO-RR approach provides a flexible and extensible mechanism to implement sustainable cloud computing, since it contributes to satisfying the objectives of green computing with the help of well-organized schedules.
Background Intangible Cultural Heritage (ICH) Chinese patterns encode millennia of cultural wisdom through complex, multi-layered semantic structures as well. Current computational approaches are unable to break down or recreate these patterns with cultural authenticity. Objective This paper presents MSD-CLIP (Multi-Granular Semantic Deconstruction and Morphological Regeneration CLIP), a new framework that aims at systematically decomposing Chinese ICH patterns into interpretable cultural values and reassembling them with accurate semantic control, without losing traditional integrity. Methods MSD-CLIP combines a hierarchical semantic deconstruction framework that examines patterns at five granularity levels: Dynasty-Temporal, Wu Xing Philosophy, Regional-Cultural, Symbolic-Functional, and Aesthetic-Compositional. It has a controllable semantic regeneration engine, which allows fine-grained manipulation of single cultural attributes, and a cross-granular consistency enforcer that ensures that regenerated patterns conform to the traditional criteria. Results Five-fold cross-validation validated model robustness (SD < 0.5%, p < 0.001 vs baselines). MSD-CLIP was tested on 3247 expert-annotated Chinese ICH patterns and demonstrated superiority over existing models on all measures. It attained 91.3 +/- 0.36% deconstruction precision, 88.7 +/- 0.42% regeneration fidelity, 4.2 +/- 0.10 cultural authenticity score, and 87.4% control accuracy. The accuracy of multi-granular deconstruction was high across all semantic levels, ranging from 87.9%-91.3%. Controlled regeneration achieved an average accuracy of 87.5% in attribute manipulation. Professional assessment indicated 84% overall approval, reliably preserving cultural authenticity. Conclusion MSD-CLIP is the first computational instrument that can deconstruct and regenerate the semantic Chinese ICH patterns at a systematic level with morphological re-inspiration. It offers a major contribution in digital heritage preservation, culturally-conscious design generation, and educational uses, without compromising the cultural integrity.
Extreme Value Theory, or univariate EVT, is widely used to assess structural risks of failure or damage, brought on by excessive environmental stressors in structural design. In engineering practice, a combination of multiple cross-correlated system components and covariates, rather than a single, univariate load, is what causes failure or damage. A multimodal state-of-the-art reliability-based approach for the multivariate structural design is presented. State-of-the-art pre-asymptotic multivariate methodology in combination with an accurate extrapolation scheme was utilized to model the Joint Probability Distribution Function (JPDF) tail of an M-dimensional random/stochastic process. The primary aim of this study was to envisage a generic, state-of-the-art multivariate reliability approach for assessing the failure or damage risks of high-dimensional dynamic systems. Note that for a multidimensional series-type system, its failure is given by a first passage event, and any parallel-type system can be equivalently reformulated as a series-type one.Novelty: The advocated multidimensional structural reliability approach would enable the extraction of relevant excessive dynamics information from time histories that had been physically recorded or numerically simulated. A variety of multimodal nonlinear dynamic systems can have their failure (damage) risks accurately and efficiently predicted using the proposed multimodal hypersurface Gaidai reliability methodology, which considers non-stationarity and memory (clustering) effects. High-dimensional, big data, deep-sea, ocean engineering and aerospace applications can benefit from the advocated multidimensional reliability approach.
A novel Life Cycle Assessment (LCA) is presented in this study to model carbon emission reductions, specifically in the construction of a power grid that favours the integration of low-carbon technologies. Unlike traditional assessments, this research alone assesses emissions from the whole construction process through planning, material procurement, transportation, installation and the operation phases of the building; and does it uniquely by comparing emissions of traditional versus energy efficient systems, smart grid implementations, low carbon materials and electric vehicles. Flow through more combinations of low-carbon technologies and more plant system cases are found to result in a significant overall emission reduction of 17.5%. Furthermore, the study compares it with existing models, such as Environmental Impact Assessment (EIA) and Carbon Footprint Analysis (CFA), and finds that life cycle analysis is a more efficient and accurate method for identifying emission-reduction potential. Therefore, this study offers an effective methodological blueprint for policymakers to develop regulations and incentives specifically tailored to incentivise sustainable construction practices and achieve global climate change mitigation and carbon neutrality.
With high penetration of renewable energy in power system, the demand side response is gradually considered to be an important flexible resource, leading to active participation in electricity market. This article provides a design of demand response trading platform for load aggregators, with integration of end-edge-cloud collaboration and blockchain. Firstly, the end-edge-cloud collaborative architecture enables distributed resources entering electricity market efficiently and realizes the privacy protection of users' production and operation data, by hierarchical decomposition and coordination of computing and data storage tasks. Meanwhile, certifying and accounting the transaction results through Consortium Blockchain technology can improve security and credibility of transaction on the Platform. Secondly, credit evaluation is taken into account when distributed resources aggregation and instructions decomposition, which encourages the user to strictly execute the contract, thus, mitigate the negative impact for aggregators. Credit evaluation is only carried out with the help of consortium blockchain, providing a secure, efficient and mutually trusted trading ecosystem.
With the acceleration of green transformation and digitalization, the integrated development of the green digital economy and the new energy industry has become the core driving force for promoting regional coordination and high-quality development. Based on the macro perspective of integrated development, this paper constructs a multi-agent game model of the coordinated evolution of the green digital economy and the new energy industry. It systematically describes the mechanisms of interactive behavior among the government, enterprises, and platform entities, driven by resource allocation, technical collaboration, and policy incentives. The model simulation results reveal that policy incentive variables are prone to causing economic pressure or regional imbalance. Based on the analysis path and research, it is shown that when the PCI (Policy Coordination Index) is 0.9, the growth rate of digital economy output value is 8.4%, and the coordination promotion factor is 2.27. The average TSS (Technology Sharing Rate) of cities with technology-sharing mechanisms is 0.655; the average FFI (Factor flow index) of the technology-support optimization group is 0.713. Improving the effectiveness of policy linkage, promoting technology-sharing mechanisms, and optimizing the flow of factors between regions are key paths to achieving deep integration between the green digital economy and new energy industries. This paper provides methodological support for the theory of integrated development, a theoretical basis, and strategic inspiration for promoting regional coordination and for formulating green transformation policies.
Printed circuit boards (PCBs) are essential components in electronic systems, providing mechanical and electrical support for various integrated elements. Due to their intricate designs, PCBs are prone to manufacturing defects such as short circuits, spurs, and unintended copper formations, which can negatively impact device functionality and reliability. This paper proposes an enhanced You Only Look Once version 8 (YOLOv8) model for detecting and localizing defects on PCBs. The proposed model integrates Coordinate Attention (CoordAtt) and a Bi-directional Feature Pyramid Network (BiFPN) into its architecture to improve detection accuracy. Additionally, it outputs annotated images and visualises the defect detection process using heatmaps to facilitate effective defect localisation. The model has been evaluated using two public benchmark datasets: DeepPCB and TDD-NET. The results of the experimental evaluations demonstrate the effectiveness of the proposed Coord-BiFPN-YOLOv8 model. It achieved on the DeepPCB dataset a mean Average Precision at an Intersection over Union (IoU) of 0.5 (mAP50) of 93.4%, and for mAP50:95 the model achieved 65.9% accuracy. Also, on TDD-NET, the proposed model achieved an impressive mAP50 of 99.1% and an mAP50:95 of 67.5%. These results highlight the model's high precision and robustness in detecting PCB defects. Additionally, the proposed model outperforms the standard YOLOv8 in defect identification, with improvements of 1.8% in F1-score and 6.4% in mAP50:95.
With the help of a smart energy hub as well as a dynamic demand response (DR) program, this study suggests a comprehensive energy management strategy for an intelligent microgrid. The main goal is to reduce pollutants as well as total operating expenses while maintaining a reliable and adaptable energy supply in face of renewable energy uncertainty. A number of dispersed energy sources, including solar panels, WTs, micro turbines, solid oxide fuel cells, CHP units, as well as battery energy storage devices, are integrated into the suggested system. Demand-side flexibility is achieved through the integration of real-time DR systems and home charging for electric vehicles. Every component is represented using detailed physical and operational constraints. The smart energy hub serves as a central coordinator for balancing energy generation, conversion, storage and consumption, while facilitating market participation. Utilizing scenario-based modeling of solar and wind variability, the Non-dominated Sorting Genetic Algorithm II is employed to create and solve a nonlinear multi-objective optimization problem. The method is evaluated using two case studies, one with DR and one without. The findings demonstrate that incorporating DR significantly lowers emissions and operational costs by 9%, while increasing renewable energy production by up to 15%. Battery scheduling effectively correlates with price changes, whilst smart energy source dispatch decreases reliance on grid imports. Under high renewable integration, the results show that the proposed coordinated strategy improves microgrid flexibility, sustainability, and economic performance.
Selecting suitable suppliers within the cracker supply chain leads to a challenging Multi-Criteria Decision-Making (MCDM) problem due to the presence of multiple, conflicting criteria such as cost, quality, distance, and reliability. Conventional models, including the integration of Pythagorean Fuzzy AHP (PF-AHP) and Pythagorean Fuzzy VIKOR (PF-VIKOR), primarily rely on linear structures. However, these methods have shortfalls in representing the circular and non-linear nature of uncertainty in expert evaluations, which can lead to imprecise outcomes. To overcome this shortfall, this research introduces a novel MCDM approach that integrates the Circular Pythagorean Fuzzy Analytic Hierarchy Process (CPF-AHP) with the Circular Pythagorean Fuzzy VIKOR (CPF-VIKOR) method for precise decision making. CPF-AHP is employed to derive the weights of evaluation criteria through circular fuzzy approach, which captures the uncertainty and inconsistency in expert opinions in a a better way. Subsequently, CPF-VIKOR is used to rank supplier alternatives by identifying a compromise solution that considers both the best possible outcome with minimal regret. The proposed CPF-based framework effectively addresses ambiguity in expert assessments and improves the accuracy of weight determination and alternative ranking. It provides a systematic and consistent approach for aggregating expert input and demonstrates superior performance compared to traditional PF-AHP and PF-VIKOR methods, particularly in handling non-linear interrelationships among decision criteria. The integration of CPF-AHP and CPF-VIKOR enhances the decision-making process for supplier selection in uncertain and complex supply chain scenarios. By effectively managing vagueness, circular aspects, and inconsistency, the proposed model delivers a more reliable and comprehensive solution. This methodology is adaptable and can be applied to other supply chain decision problems with similar complexity and uncertainty.
Major health emergencies often trigger rapid changes in public risk perception, amplified by the widespread dissemination of information through intelligent networks. Accurately capturing these dynamics and assessing the authenticity of online information is crucial for effective crisis management. This research proposes a data-driven framework to analyze the dynamic evolution of public risk perception and the reliability of information dissemination during viral disease emergencies. The approach integrates association rule mining with an Artificial Lizard Search-driven Attention-refined Recurrent Neural Networks (ALS-Att-RNN) model to identify the public perception key risk factors and key risk chains of different risk levels, as well as to evaluate the public opinion situation. To ensure reliable model performance, data preprocessing was carried out using Z-score normalization, which standardized the input data and enhanced the accuracy of subsequent analysis. An association rule mining algorithm is designed to handle non-Boolean and continuous data, enabling the extraction of significant correlations among misinformation signals, key viral risk indicators, and public perception trends. The framework identifies critical misinformation chains, trustworthy information sources, and key perception risk factors, providing a quantitative evaluation of information authenticity across social and intelligent network platforms. Empirical results show that the proposed method improves assessment accuracy (0.98) compared to conventional models. This framework provides a computationally efficient and interpretable tool for health authorities to identify misinformation chains, key perception risk factors, and public response patterns, supporting timely interventions and guiding informed decision-making to mitigate viral disease risks.
This study proposes an integrated framework combining biomechanics, spatial econometrics, and machine learning to enhance sustainable livestock management. Using panel data from 11 provinces over a two-decade period, we analyse the nonlinear relationship between livestock concentration and regional economic output using Cobb-Douglas functions and dynamic panel regression with GMM estimation. Location entropy indices reveal spatial disparities in livestock agglomeration, while robustness is ensured via VIF, root tests, and LSDVC correction. A case study on meerkat behavior demonstrates the effectiveness of a hybrid SVM model in classifying key behaviors-vigilance, resting, foraging, and running-from over 82,000 labeled video bouts. The model achieves high classification accuracy and interpretability. Results highlight the synergy of biomechanical analysis and intelligent classification in improving animal welfare monitoring, while spatial models inform balanced regional livestock development. The proposed framework offers actionable insights for eco-agriculture policy, smart farming systems, and sustainable rural economic planning.
In the era of smart technology, the integration of electronics with textiles has given rise to intelligent clothing garments embedded with electronic systems capable of sensing, processing, and responding to environmental or physiological data. However, ensuring both sensor accuracy and user comfort remains a significant challenge. To address this, the present research aims to design and implement an embedded smart clothing system that combines wearable sensor technology with soft-textile materials to enable real-time monitoring of body temperature while maintaining high wearing comfort. The proposed system utilises strategically placed, skin-loose temperature sensors embedded into infant-friendly fabrics, minimising irritation and enhancing mobility by eliminating the discomfort commonly associated with skin-tight sensors. Custom-designed smart garments equipped with these sensors were used for data collection, followed by pre-processing techniques such as normalisation and one-hot encoding. At the core of the system lies a low-power embedded microcontroller that wirelessly collects, processes, and transmits sensor data, ensuring energy-efficient operation suitable for everyday infant wear. To address the reduced precision of individual loose sensors, a deep learning-based temperature estimation model was developed. This model aggregates multichannel sensor inputs and processes using a Komodo Mlipir fine-tuned Stochastic Temporal Convolutional Network (KM-STCN), enabling real-time estimation and correction of body temperature readings. Implemented using Python, the experimental results demonstrate that the combined use of multiple skin-loose sensors and the deep learning model achieves high temperature estimation accuracy, recall, F1-score, and precision rates exceeding 93%, while significantly enhancing garment comfort. This research highlights the potential of integrating embedded systems, infant garment design, and AI-based signal processing to develop intelligent clothing that effectively balances health-monitoring functionality with everyday wearability in healthcare technologies.
This investigation aims to explore users' accuracy in identifying computer mice designed with the golden ratio and their aesthetic preferences, with a specific focus on gender differences. First, the study used a database of 233 Genius brand mice and selected five samples with length-to-width ratios ranging from 1.42 to 1.78. A total of 99 university students (62 females and 37 males) participated in absolute and relative judgment tests. Results showed that most participants failed to correctly identify the mouse with a ratio closest to the golden ratio (approximately 1:1.62), even when provided with 2D or 3D visual references. The chi-square test showed that the difference in recognition accuracy between males (32%) and females (34%) was not statistically significant (p = 0.34). In addition, there was no correlation between aesthetic preference and whether the mouse followed the golden ratio. These findings suggest that golden ratio design is neither easily recognizable nor a key factor in product preference, and designers need not overly rely on it to enhance product appeal. Finally, polynomial regression analysis was employed to investigate the effect of the ratio on the preferences of all participants. The model fit was very high (R2 = 99.91%, p-value = 0.038), showing that the mouse ratio was associated with all participants' preferences. According to the results, if a ratio/preference prediction system for mouse design is developed in the future, a mouse design with an aspect ratio of 1.5 to 1.56 can serve as an important reference indicator for preference prediction.
This article investigates decentralized blockchain technology to improve the management of sustainability in prefabricated buildings. By utilizing a public blockchain and a private blockchain together, both real-time recording and management of construction-related data, and contract management-applied automation are accomplished. Smart contracts, Merkle trees, and Proof of Work (PoW) consensus mechanisms guarantee the security, reliability, and transparency of data, especially with respect to those categories including material qualifications, construction progress, and quality inspections. The Hyperledger Fabric platform incorporates encryption technology combined with smart contracts for additionally protecting the privacy of financial data, construction progress, and supply chain data through use of the AES-256 algorithm encryption and strict data audit monitoring controls. Moreover, decentralized blockchain technology improves transparency and controllability of data systems, effectively decreasing the risk of data compromise, and optimizing the management of the supply chain through smart contracts, hash value technology, and other means. Tracking time through the average supply chains is 6.1 s, the average accuracy for fulfillment of contracts is 98.36%, the average success rate for smart contract execution is 98.84%, and the average timeliness of information flow is 95.12%. This article promotes the digital transformation and sustainable development of the construction industry by improving the prefabricated construction sector's capacity for sustainable development and offering fresh concepts for data management and monitoring.
Traditional rule-based systems struggle with complex power grid faults due to limited flexibility and dynamic response. This study proposes an automated fault disposal approach using a power grid fault knowledge graph combined with reasoning algorithms. The system models equipment, fault types, and disposal steps as a graph, where nodes represent entities and edges denote fault relations. Real-time monitoring data feeds into the graph, enabling automated reasoning to identify fault causes, affected areas, and disposal options. Based on this, the system dynamically generates accurate disposal plans with high automation, reducing manual intervention, and supports over 80% of fault types with a maximum response time of 3.5 s, significantly improving diagnosis accuracy and emergency response efficiency. This method offers a robust technical path for intelligent grid operations.
As the secondary ship market becomes increasingly active, achieving precise ship valuation while fully accounting for market fluctuations has grown increasingly important. This paper establishes a price assessment framework integrating static valuation with dynamic market adjustments: the static component employs an ACO-FA-optimized BP neural network to derive benchmark prices based on individual vessel characteristics; the dynamic component constructs a price index combined with oil prices and freight rates into a multivariate time series. A GRU model captures market adjustment factors, which are then fused with static valuations via Kalman filtering to generate final transaction prices. Empirical results based on Chongqing's 2020-2025 dry bulk carrier transaction data demonstrate that this model significantly enhances second-hand vessel price assessment while simultaneously delivering static valuations, market adjustments, and composite prices. It provides buyers and sellers with a well-explained quantitative basis for pricing, negotiation, and investment decisions across varying market conditions.
This study examines the cost pass-through behavior in China's retail electricity market by comparing vertically integrated and independent retailers. Using a unique monthly panel dataset covering 34 provincial regions and 340 retail pricing plans) 10 retailers x 34 province) from 2018 to 2023, the study disaggregates upstream electricity costs into generation costs proxied by futures market prices and regulated transmission and distribution charges ("lines costs"). The empirical analysis, based on fixed effects panel regressions, reveals that independent retailers exhibit significantly higher pass-through rates, particularly for generation-related expenses, while vertically integrated firms demonstrate pricing stability due to internal hedging strategies. The findings further indicate that lines costs are more uniformly passed through across all firm types due to their regulated and predictable nature. Transmission length, transformer size, and customer population are important infrastructure factors influencing the price of electricity. This research sheds light on the firm's structural configuration, the regulatory environment and the firms costs and how the three are interrelated to explain price behavior under mixed markets for electricity. The contribution of the study will inform policymakers on the issue of electricity market liberalization and the necessary tools to implement it. This is particularly important for developing countries that are shifting to the liberalized competitive retail model.
Liquefied Petroleum Gas (LPG) is widely used in households, commercial establishments industrial sectors. LPG is highly flammable and poses a risk of explosion if not handled properly. Hence, an early gas leakage detection system is crucial for ensuring safety in various environments. In this article, a Deep Learning (DL)-based LPG Gas leakage detection and Alert system named Quasi-Recurrent Neural Network with Addax Wolf Bird Optimization Algorithm (QRNN_AWBOA) is proposed using the gas leakage data. In this approach, the median normalization method is utilized to normalize the raw data. Then, a fusion model named Deep Neural Network (DNN) with Neyman similarity is utilized for the feature fusion process. Then, the data is augmented using oversampling. Later, the detection process is carried out using the Quasi-Recurrent Neural Network (QRNN) model. The QRNN effectively trained the Addax Wolf Bird Optimization Algorithm (AWBOA). Finally, an alert is sent to the NG112 authorities if the leakage is present. This detection system attained the lowest Mean Squared Error (MSE) of 0.016, Mean Absolute Percentage Error (MAPE) of 0.056, Root Mean Squared Error (RMSE) of 0.128, and Weighted Absolute Percentage Error (WAPE) of 0.057.
Intelligent Tutoring Systems face significant challenges in dynamically adapting to diverse student cognitive states (e.g., knowledge gaps, learning pace, and engagement levels) while maintaining high confidence in pedagogical decisions. Traditional rule-based or static Machine Learning (ML) models often fail to generalize across different learners and subjects. A confidence-aware Meta-Reinforcement Learning (Meta-RL) framework is proposed, allowing for fast adaptation to individual student needs with quantifiable uncertainty estimation. The Proposed Method (PM) leverages meta-learning to pre-train a policy on a distribution of simulated and real-world student interactions, allowing rapid fine-tuning with minimal data for new learners. The framework incorporates Bayesian Neural Networks (BNN) to assess prediction confidence, ensuring that tutoring actions (e.g., hint provision or problem difficulty adjustment) are personalized and reliable. Experiments on large-scale educational datasets (e.g., ASSISTments, MOOC logs) demonstrate that the model outperforms baseline methods (e.g., deep RL, non-adaptive ITS) in learning gain (+12%) and student engagement (+18%), with real-time deployment feasibility on edge devices. This work bridges the gap between high-speed adaptation and high-confidence AI in education, offering a scalable solution for next-generation ITS.
