Introduction: The 6G world requires connected intelligence, but there is a crucial paradox between the standards of Large Language Model (LLM) and edge constraints. The premium devices have up to 6- 12GB of DRAM, whereas the typical 175B models need 350GB of storage, which is 30 times that of the premium version. Literature Survey: It has been proposed that the bandwidth can be reduced by 90 % with Semantic Communication (Scom) and Edge Semantic Cognitive Intelligence (ESCI). Besides, neuromorphic-based Spiking Neural Networks (SNNs)-model quantization (INT4/INT8) are also known to be necessary to achieve order-of-magnitude energy efficiency (J/token) on resource-constrained hardware. Methodology: This paper proposes a Hybrid Cognitive Model utilizing a three-tier CloudEdge-Device hierarchy. The model integrates event-driven neuromorphic principles with self-optimizing resource management, utilizing paged KV-cache and resource-aware agents for dynamic task offloading. Results: Quantitative evidence is used to show that the hybrid strategy helps to address the 30x resource gap by attaining a 10-100x energy-per-token efficiency due to event-driven neuromorphic sparsity. Statistical analysis makes it evident that semantic filtering substantially reduces communication overhead and maintains reasoning faithfulness by 90 %, and, effectively, it keeps the thermal conditions of devices stable in the case of prolonged 6G edge communications. This model can be used to make sustainable and multi-step thinking on the edge. The hybrid solution achieves the 6G vision of pervasive intelligence by bridging the hardware-software gap via cross-layer co-design.

In this work, a new optimization approach for the exploitation and smooth integration of hybrid renewable sources (HRs), including PV solar/wind turbines in addition to a dynamic reconfiguration process of electricity distribution microgrids, is proposed. A crucial novelty of this work is the definition of a multi-scenario optimization framework that allows to compare devices at various levels (of complexity) across different system conditions, and which has not been thoroughly investigated yet in the literature. Further, the study presents one of the most detailed and operational-realistic representations of an IEEE 84-bus Taiwan Power Company (TPC) distribution system model (in a unique dataset containing exact switch status, impedance properties, and power injection location). This network model serves as a scalable benchmark for grid optimization studies and utility-scale PV deployment. Moreover, the proposed method adopts a variant of particle swarm optimization algorithm to minimize the operational cost along with the variance-based penalty function in consideration of uncertainty associated with renewable power generation. This combination of cost effectiveness and uncertainty management in the context of a single objective function increases the stability and flexibility in grid functions. Then, the approach is verified for three operational modes: a base scenario without any renewable integration, a PSO-tuned scenario with PV and WT but ignoring network reconfiguration, and an integrated (renewables together with reconfiguration). The final formation achieved after optimization can minimize the power losses from 4.924 MW to around 0.002 MW and reduce the operational cost to $1.954/MWh, as reported in results. Such results validate the effectiveness of our proposed strategy for facilitating cost-efficient and robust operation of smart grid

Manufacturing systems that are technology-intensive have high interdependencies between financial decisions on investments, the change in production capacity, and operational efficiency. Traditional methods tend to look at financial and operational planning in isolation, resulting in poor performance of the system and poor use of resources. In order to overcome this shortcoming, this paper has suggested a combined techno-economic optimization framework, which models financial performance, production planning, technology-based capacity development, and energy efficiency together in a single mathematical expression. The manufacturing system is modeled in terms of a multi-period constrained optimization problem, in which the investment of technology, production output, capacity variation, and energy consumption are all optimized. A multi-objective function that combines financial and operational functions is formulated using diversity of weights, and an algorithm to find a solution is presented to achieve computational feasibility. The framework proposed is assessed by the numerical simulation in a 5-period planning horizon. Findings show that when there is a technology investment, capacity is incrementally expanded between 100 and 180 units at production levels that are viable. The integrated strategy has a total financial performance of 742.6 with a return on investment of 1.48 and average capacity utilization of 0.82. The energy efficiency is increasing to an average of 2.91, which shows that efforts are made to plan production considering energy efficiency. Sensitivity analysis also reveals that an increase in the technology gain coefficient will increase the Technical-Economic Performance Index to a maximum of 3.24, followed by a decreasing marginal gain. On the whole, the findings prove that the suggested framework offers a powerful and technologically efficient decision-support tool that can be applied to streamline financial and operational performance in technology-intensive production systems.

Micro-expressions (MEs) are involuntary facial expressions, short-lived (usually between 1/5 and 1/25 seconds), and important in the application of security, psychological tests, and forensics. The MEs are however difficult to identify because it occur quickly and also involve little movement of the muscles. The paper presents an Enhanced Micro-Expression Descriptor which incorporates Gabor Wavelet-based Local Binary Patterns (GW-LBP), Temporal Gradient Magnitude Histograms (TGMH), and Wavelet Transform (WT) to enhance ME recognition, by overcoming the weaknesses of traditional methods in illumination sensitivity and poor computing power. The algorithm involves the use of GW-LBP to extract spatial texture, TGMH to capture changes in temporal motion, and WT to analyze frequencies on a multiscale basis. This is achieved by classifying the fused feature set with an RBF kernel Support Vector Machine (SVM), which is optimized by down-sampling to a size manageable by resources (4096 dimensions) to provide a resource-efficient, real-time solution with application in edge computing. Benchmark dataset experimental results prove that the proposed method is better than the existing techniques with a recognition accuracy of 85.9%. This is a major boost compared to conventional procedures such as the LBP-TOP (67.5%) and CNN-based models (78.3%). Also, the Wavelet Transform option exploited the highest score in entropy (0.93), which implies that it can be highly used in real-time behavioral analysis, emotion detection, and security surveillance. The findings affirm that the hybrid approach, which incorporates spatial, temporal, and frequency characteristics, has a better performance than the existing ME recognition models.

The global construction industry accounts for approximately 37% of energy-related carbon dioxide emissions and nearly one-third of global waste, necessitating a rapid shift toward sustainable practices. Building Information Modeling (BIM) has become a transition catalyst, going beyond simple 3D visualization that has incorporated environmental intelligence. This essay explores the central aspect of BIM to the development of sustainable building by looking at its multi-dimensional features with particular reference to the 6D BIM (Sustainability). Statistical insights indicate that integrating BIM during the design phase can reduce material waste by 15% to 25%, with high-performance case studies like The Edge achieving reductions of up to 70% through precise quantity take-offs and automated clash detection. Furthermore, BIM-driven energy simulations enable architects to optimize building envelopes, potentially reducing operational energy consumption by up to 30%. By facilitating automated Life Cycle Assessments (LCAs), BIM enables the comparison of low-carbon material alternatives, which can reduce a project's total embodied carbon by approximately 20%. Despite these established benefits, the study reveals that there are major obstacles to its widespread use, such as a lack of interoperability and a high initial learning curve. However, the analysis concludes that the long-term Return on Investment (ROI) driven by a 5% to 8% reduction in total project costs and enhanced building performance positions BIM as an indispensable tool for achieving global net-zero targets. The results indicate that the shift to dynamic Digital Twins of the models currently in place will reduce the difference in the predicted and observed environmental performance, so that sustainability will become a tangible reality, rather than a goal that is to be achieved at the design stage.

This study presents a bibliometric analysis of maritime tourism transportation management and development, emphasizing Malaysia’s contribution to global scholarship. Data were extracted from the Scopus database using title-based searches covering maritime tourism, island tourism, and transportation studies. A sample of 176 publications (1983 2022) was reviewed with Harzing Publish or Perish, VOSviewer, and Microsoft Excel and screened and validated through PRISMA. Findings reveal that there has been a consistent increase in the volume of publications since 2010 with Malaysia ranked as the top countries in the world (20.95%), aided by existing institutions like Universiti Sains Malaysia (USM) and Universiti Malaysia Terengganu (UMT). It is a moderately influential field with a moderate number of citations (1,785 citations, h-index = 23) and the concentration of themes in sustainability, tourism management, and ecotourism. New issues are emerging such as climate change, marine life, and green mobility, which have switched to sustainability and post-pandemic resilience. The results of the study indicate that Malaysia is emerging as an academic leader in maritime tourism, but also that the gap in research on the topic of transportation safety, digital transformation, and low-carbon mobility exists. This work has the advantage of providing the first detailed bibliometric mapping of the subject area that will help to inform policy, enhance regionalism, and align the maritime tourism strategies of Malaysia with the Blue Economy and Sustainable Development Goals (SDGs 8, 13, 14).

The smart infrastructure construction projects implemented under the Engineering-ProcurementConstruction (EPC) model have been growing more complicated with the globalized supply chains, the technology-induced complexity of the components employed, and the increased sustainability and risk management demands. Conventional EPC supply chain and project management have been highly divided, resulting in a lack of real-time visibility, reactive decision-making, and inefficiencies in performance. This paper opposes this by analyzing how digital supply chain practices can be incorporated into project management functions to enhance performance in EPC-based smart infrastructure projects. The research follows a conceptual and analytical course, integrating the latest literature on digital supply chains, project delivery of EPC, and technologies of smart infrastructure. It is on this synthesis that a Digital Supply Chain- Project Management Integration Framework is created with the aim of matching digital capabilities in terms of engineering, procurement, construction, and governance. Analyses of comparative performance based on literature-synthesized measures prove that digitally enabled EPC supply chains obtain significant improvements in comparison with the traditional practice in terms of approximately 35-40 % procurement lead time, 45-50 % cost variance, and 45-60 % risk response time. Other benefits can be seen in schedule reliability and supply chain visibility; the largest performance improvements can be seen within the construction phase and the project governance phase. The results indicate that digital integration contributes to the proactive management of risks, enhanced coordination, and performance-based project governance. The current study is an addition to the literature by filling the gaps between digital supply chain management and project management in the EPC setting and providing a practical understanding in furthering the resilience, transparency, and sustainability of smart infrastructure provision.

The concept of monitoring conditions with the help of AI has become a significant aspect of Industry 4.0 that enhances machine reliability and provides predictive maintenance. However, the models of anomaly detection based on deep learning are not readily implemented because of their lack of interpretability. The article introduces a novel anomaly detection model of vibration signals using a Transformer and augmented with Shapley Additive exPlanations (SHAP) to provide the accountability of the model. To improve the power of the model in diverse circumstances, the hybrid approach of Wavelet Transform and Variational Mode Decomposition (WT-VMD) preprocessing technique is used to get meaningful time-frequency features. The proposed model was tested on an industrial vibration dataset, and the accuracy of anomaly detection is 99.2%, and the fidelity of SHAP elucidation is 88%. An experiment that used 50 industrial maintenance experts as the subjects showed that the level of trust grew by 45 % and the decision-making process became 30 times faster using explainable exploratory models than using non-explainable models. The results illustrate that the Transformer-based method is more effective in increasing the detection performance and interpretability, which is required in industrial predictive maintenance. This model allows implementing AI in industrial systems by defining fault detection in a clear way that facilitates the realization of the maintenance plans and makes it more reliable. The paper has demonstrated the potential of the application of deep learning, along with an interpretable model, in solving the issue of fault diagnosis and condition monitoring in the complicated industrial environment.

The enterprises, which are science and technology-based (STBEs), work in a highly dynamic and knowledge-intensive environment where long-lasting competitiveness is determined by the success in matching innovation and marketing potential. Although these capabilities have been studied separately in previous research, a noticeable lack of empirical research has incorporated these two capabilities into a single framework that explains their combined effect on innovation performance. The paper is a comparative study that empirically explores the boundary of innovation management capabilities and marketing capabilities on innovation performance in STBEs, with absorptive capacity discussed as a mediating effect. The survey design that was used was quantitative, cross-sectional, and data were gathered among managers of science and technology-based enterprises. The relationships proposed were tested with the help of structural equation modeling. These findings suggest that there is a strong positive impact of the innovation management capabilities on innovation performance (0.32, p < 0.001) and the marketing capabilities have a strong positive impact on innovation performance (0.29, p < 0.001). It was established that innovation management capabilities (β = 0.14, p < 0.001) and marketing capabilities (= 0.13) have significant indirect effects, mediated by absorptive capacity (0.001). Besides, the innovation performance has a critical positive impact on the firm performance (= 0.47, p < 0.001). The model describes 58 % of the innovation performance and 44 % of the firm performance variance. The results are a reflection of the significance of combining innovation management, marketing, and knowledge based capabilities to improve the effects of innovation within science and technology-based businesses.

The study will be conducted to complement the diagnosis of proximal caries lesions that are difficult to improve due to their location between the teeth, as shown in Near-Infrared Light Transillumination (NILT) photographs. It was proposed to enhance caries detection with a semantic segmentation model based on YOLOv11, which is more specific in detecting mesial and distal caries lesions, which were not adequately explored in previous literature. The model was trained on a sample of 440 augmented grayscale NILT images collected from 17 patients at the Faculty of Nursing, King Abdulaziz University, Jeddah, Saudi Arabia. These pictures were categorized into five groups, i.e., enamel, dentin, background, mesial caries, and distal caries. The significance of the classes was addressed by optimizing hyperparameters and class weights for mesial and distal caries. The YOLOv11 model had an overall Dice coefficient of 87 and the highest scores of enamels (80) and dentin (89) due to their low prevalence in the dataset (mesial and distal caries, respectively). However, the model was deemed very specific, and its negative predictive value was 0 in all classes. The findings indicate that a well-represented class is the most effective with the model, whereas mesial and distal caries need further improvement. Future work will focus on improving the model's performance on underrepresented classes through data augmentation and class-balanced training, ultimately enhancing its clinical applicability for the intricate, non-invasive detection and diagnosis of caries.