Search Results (465)

The upstream oil and gas (O&G) industry faces persistent challenges, including volatile oil prices, declining reserves, and the increasing prominence of renewable energy sources. In response, the Indonesian government has set an ambitious target to increase national O&G production by 70% by 2030. This goal requires upstream O&G producers to adopt innovative approaches that enhance performance and resilience. This study emphasizes organizational agility as a critical capability for organizations in VUCA environments to remain resilient and competitive. This study examines the influence of relevant agility enablers on Indonesian upstream O&G, ensuring that no critical factors are overlooked in the implementation of agility. The human–technology–organization (HTO) framework was used to conceptualize and examine its role in supporting organizational agility. Data were collected from 103 managerial-level respondents representing 27 producer companies representing more than 75% of Indonesia’s overall O&G production. PLS-SEM was employed to examine whether relationships existed among predictor variables and organizational agility. The results highlight HTO, leadership, and innovation capacity as significant enablers of organizational agility. This study contributes theoretically and practically by integrating the HTO framework into the agility discourse and offering a comprehensive view of agility enablers that foster transformation, resilience, and sustainability of Indonesia’s upstream O&G sector. Full article

This study systematically investigates the key parameters governing the mechanical performance of fly ash-based geopolymer across paste, mortar, and concrete scales. Comprehensive mechanical testing, combined with SEM and MIP analyses, elucidated the relationships between activator composition, pore structure, and strength development. A key innovation is the development of a cross-scale quantitative framework linking mortar strength to concrete compressive strength, enabling preliminary predictive capability across material scales. Grey relational analysis identified curing temperature as the most influential factor, followed by SiO₂/Na₂O and H₂O/Na₂O ratios. Thermal curing accelerates strength development and temperatures of 70~80 °C markedly enhance reaction rates. Both compressive and flexural/splitting tensile strengths increase and then decrease with NaOH concentration or sodium silicate modulus, with optimal performance at 24~26% NaOH and SiO₂/Na₂O ratio of 1.2~1.4, while increasing H₂O/Na₂O reduces strength nearly linearly, constrained by workability. Concrete compressive strength rises with coarse aggregate content up to 60~70% before declining. SEM and MIP confirm that optimal activator formulations produce a dense, homogeneous gel matrix with lower porosity and fewer unreacted particles. Strong square-root correlations between compressive and tensile-related strengths were observed across all material systems. Overall, this work establishes a quantitative foundation for geopolymer mix design and provides actionable guidance for developing high-performance, low-carbon geopolymer concrete. Full article

Low-cost sensors (LCSs) have rapidly expanded in urban air quality monitoring but still suffer from limited data accuracy and vulnerability to environmental interference compared with regulatory monitoring stations. To improve their reliability, we proposed a machine learning (ML)-based framework for LCS correction that integrates various meteorological factors at observation sites. Taking Tongshan District of Xuzhou City as an example, this study carried out continuous co-location data collection of hourly PM_2.5 measurements by placing our LCS (American Temtop M10+ series) close to a regular fixed monitoring station. A mathematical model was developed to regress the PM_2.5 deviations (PM_2.5 concentrations at the fixed station—PM_2.5 concentrations at the LCS) and the most important predictor variables. The data calibration was carried out based on six kinds of ML algorithms: random forest (RF), support vector regression (SVR), long short-term memory network (LSTM), decision tree regression (DTR), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), and the final model was selected from them with the optimal performance. The performance of calibration was then evaluated by a testing dataset generated in a bootstrap fashion with ten time repetitions. The results show that RF achieved the best overall accuracy, with R² of 0.99 (training), 0.94 (validation), and 0.94 (testing), followed by DTR, BiLSTM, and GRU, which also showed strong predictive capabilities. In contrast, LSTM and SVR produced lower accuracy with larger errors under the limited data conditions. The results demonstrate that tree-based and advanced deep learning models can effectively capture the complex nonlinear relationships influencing LCS performance. The proposed framework exhibits high scalability and transferability, allowing its application to different LCS types and regions. This study advances the development of innovative techniques that enhance air quality assessment and support environmental research. Full article

Recent comprehensive research (2023–2024) on basalt fiber-reinforced composites (BFRCs) has meticulously documented significant progress across diverse applications, including protective coatings, high-performance concrete, reinforcement bars, and advanced laminates. The central theme of these developments revolves around innovative composite design strategies that strategically incorporate basalt fibers to markedly enhance mechanical properties, durability, and protective capabilities against environmental challenges. Key advancements in synthesis methodologies highlight that the integration of BFs substantially improves abrasion and corrosion resistance, effectively inhibits crack propagation through superior fiber-matrix bonding, and confers exceptional thermal stability, with composites maintaining structural integrity at temperatures of 600–700 °C and demonstrating short-term resistance exceeding 900 °C. The underlying mechanisms for this enhanced performance are attributed to both chemical modifications—such as the application of silane-based coupling agents which improve interfacial adhesion—and physical–mechanical interlocking between the fibers and the matrix. These interactions facilitate efficient stress transfer, leading to a breakthrough in the overall multifunctional performance of the composites. Despite these promising results, the field continues to grapple with challenges, particularly concerning the long-term durability under sustained loads and harsh environments, and a notable lack of standardized global testing protocols hinders direct comparison and widespread certification. This review distinguishes itself by offering a critical synthesis of the latest findings, underscoring the immense application potential of BFRCs in critical sectors such as civil engineering for seismic retrofitting and structural strengthening, the automotive industry for lightweight yet robust components, and advanced passive fireproofing systems. Furthermore, it emphasizes the growing, innovative role of simulation techniques like finite element analysis (FEA) in predicting and optimizing the performance and design of these composites, thereby providing a robust scientific foundation for developing the next generation of high-performance, sustainable structural components. Full article

This study examines how digital transformation enhances supply chain resilience among Chinese firms, with a focus on the underlying mechanisms and contextual conditions. Grounded in dynamic capabilities theory, we conceptualize supply chain resilience along two dimensions: proactive capability and reactive capability. Using data from A-share listed companies between 2007 and 2022, we construct firm-level resilience measures through entropy weighting. Digital transformation is measured by textual analysis of corporate annual reports, supplemented with policy documents and academic literature to enrich the keyword dictionary. Empirical results, validated through instrumental variable estimation, Heckman two-stage models, and multiple robustness checks, show that digital transformation significantly improves overall supply chain resilience, with a stronger effect on reactive capability. Further analysis identifies three mediating channels: improved information sharing across the supply chain, enhanced firm-level innovation, and reduced exposure to environmental uncertainty. Heterogeneity tests reveal that the positive impact of digital transformation is more pronounced in non-state-owned enterprises, high-tech firms, and firms in technology-intensive or labor-intensive industries. The effect is also stronger for firms operating under high environmental uncertainty or located in regions with lower levels of marketization. These findings offer practical guidance for managers and policymakers aiming to strengthen supply chains through digitalization, particularly in an era marked by growing global disruptions and sustainability challenges. Full article

Thermal elongation in high-speed motorized spindles constitutes a major source of machining error in five-axis machine tools, critically impacting machining precision. This study aims to develop and validate a cumulative thermal error compensation model for predicting spindle thermal elongation, subsequently enabling effective compensation via a dedicated control algorithm. Key thermal error factors, primarily spindle speed and cumulative thermal error, were identified through analysis. An innovative numerical prediction model incorporating these factors was established. Its performance was evaluated through experiments utilizing eddy-current displacement sensors for high-speed, high-precision thermal elongation measurement. The validation results demonstrated the model’s strong predictive capability: During spindle startup, prediction errors exhibited minor transients, stabilizing near zero once the operating speed was reached. Under dynamic speed changes, the maximum prediction error was only 1.28 μm, with the overall maximum residual error recorded at 2.04 μm. These findings confirm the model’s high accuracy. Furthermore, the model exhibits excellent generalization capability, delivering significant compensation effectiveness across diverse variable-speed operating conditions. This work successfully developed a highly accurate numerical model and a practical compensation strategy, significantly enhancing the positioning accuracy of high-speed spindles against thermal disturbances. The proposed approach offers substantial engineering utility for thermal error compensation in precision machining applications. Full article

Artificial intelligence (AI) is transforming pharmaceutical science by shifting drug delivery research from empirical experimentation toward predictive, data-driven innovation. This review critically examines the integration of AI across formulation design, smart drug delivery systems (DDSs), and sustainable pharmaceutics, emphasizing its role in accelerating development, enhancing personalization, and promoting environmental responsibility. AI techniques—including machine learning, deep learning, Bayesian optimization, reinforcement learning, and digital twins—enable precise prediction of critical quality attributes, generative discovery of excipients, and closed-loop optimization with minimal experimental input. These tools have demonstrated particular value in polymeric and nano-based systems through their ability to model complex behaviors and to design stimuli-responsive DDS capable of real-time therapeutic adaptation. Furthermore, AI facilitates the transition toward green pharmaceutics by supporting biodegradable material selection, energy-efficient process design, and life-cycle optimization, thereby aligning drug delivery strategies with global sustainability goals. However, challenges persist, including limited data availability, lack of model interpretability, regulatory uncertainty, and the high computational cost of AI systems. Addressing these limitations requires the implementation of FAIR data principles, physics-informed modeling, and ethically grounded regulatory frameworks. Overall, AI serves not as a replacement for human expertise but as a transformative enabler, redefining DDS as intelligent, adaptive, and sustainable platforms for future pharmaceutical development. Compared with previous reviews that have considered AI-based formulation design, smart DDS, and green pharmaceutics separately, this article integrates these strands and proposes a dual-framework roadmap that situates current AI-enabled DDS within a structured life-cycle perspective and highlights key translational gaps. Full article

Global climate change presents severe challenges to both ecosystems and human societies, necessitating profound structural adjustments in high-emission industries, such as petroleum. This paper develops a two-tier supply chain model comprising a single raw material supplier (leader) and a single refining manufacturer (follower) to examine the effects of R&D cost subsidies and per-unit emission reduction subsidies on supply chain equilibrium decisions and social welfare under three cooperation modes: non-cooperative, cost-sharing, and full cooperation. The results indicate that both types of subsidies effectively elevate wholesale prices, retail prices, emission reduction levels, and supply chain profits. However, per-unit emission reduction subsidies prove more effective in stimulating short-term emission reductions by manufacturers, whereas R&D cost subsidies are more conducive to fostering long-term technological innovation and enhancing emission reduction benefits across the supply chain. Furthermore, social welfare is significantly higher under the R&D cost subsidy strategy than under the per-unit emission reduction subsidy. The study also demonstrates that the full cooperation mode achieves optimal emission reduction performance in the supply chain, and the R&D cost subsidy strategy strengthens overall supply chain profitability and market competitiveness. When designing subsidy policies, governments should account for the roles, technological capabilities, and cost structures of supply chain members, prioritize full cooperation modes, and mitigate risks associated with cost-sharing models under high subsidy intensities to maximize policy effectiveness. Full article

In this study, we propose a multi-scale feature fusion network based on an improved RT-DETR model for the efficient detection of tomato leaf disease. Our model combines the multi-scale extended residual module by capturing contextual information at various scales and the multi-scale feature pyramid network by integrating feature information from different levels, which improves feature extraction capability and reduces the interference of complex backgrounds on feature extraction, thereby improving information transmission efficiency and the accuracy of the model. In addition, the novel loss function called adaptive focal loss (AFL) was used, which is based on traditional focal loss with the introduction of attenuation factors to focus the model’s attention to high-loss features to alleviate overfitting and of dynamic weight adjustment mechanisms to focus on the more important features during the training process to improve the overall learning performance. Another significant advantage of AFL is that it can more efficiently improve the detection accuracy on imbalanced datasets than on balanced datasets. These innovations optimized the learning strategy of the model, making AP@0.50 up to 97.9% on detecting the categories of tomato diseases. In addition, this model also achieves the high detection accuracy of 85.4% on other crop diseases. These results provide valuable references for agriculture applications. Full article

Unmanned aerial vehicles (UAVs) have emerged as a pivotal technology for enhancing the agility and resilience of future wireless networks. However, conventional optimization approaches remain predominantly reactive, relying solely on current network conditions for decision making. This proves to be inadequate for handling sudden traffic surges in dynamic environments, resulting in suboptimal service quality. To address this limitation, this paper proposes a novel joint optimization framework integrating spatiotemporal traffic prediction. This equips UAVs with predictive capabilities, thereby facilitating a paradigm shift from passive response to proactive service provision. The main contributions of this work are fourfold: First, a novel closed-loop optimization framework is introduced, deeply integrating an advanced traffic-forecasting module with a communication resource optimization module to provide a systematic, forward-looking decision-making solution for UAV-assisted communications. Second, a cellular traffic predictor based on Gaussian mixture model meta-learning (GMM-ML) is designed. This model effectively captures the periodicity and heterogeneity of traffic data, enabling the precise prediction of future hotspot areas and resolving the challenge of accurate forecasting under small-sample conditions. Third, a long-term discounted mixed-integer nonlinear programming (MINLP) problem model is formulated. This innovatively incorporates a “service readiness reward” for predicted hotspots within the objective function to achieve long-term performance optimization. Fourth, an efficient and convergent predictive iterative association and location optimization (P-IALO) algorithm is developed. Utilizing block coordinate descent and continuous convex approximation techniques, this algorithm decomposes the original complex problem to alternately optimized subproblems of user association and trajectory planning, guaranteeing algorithmic convergence. To validate the effectiveness of the proposed framework, large-scale simulation experiments were conducted using real-world traffic data. The results demonstrate that compared to traditional reactive algorithms, the proposed scheme significantly enhances the overall system throughput by 12%, improves user QoS satisfaction by 9.4%, and reduces service interruptions by 34.2%. Concurrently, the algorithm exhibits favorable convergence speed and robustness, maintaining performance advantages even under predictive errors. Extensive experimentation thoroughly demonstrates the efficacy of this research in enhancing the performance of drone-assisted networks. Full article

Plant-derived vesicles (PDVs) represent an emerging class of naturally bioformulated nanocarriers with potential nutraceutical and therapeutic applications. In this study, the multifunctional biological activity of PDVs obtained from Eruca sativa leaves (arugula leaf vesicles, ALVs) was investigated both in vitro and in vivo. In differentiated Caco-2 and HepG2 cells, ALVs exhibited significant antioxidant activity, being rich in polyphenols and organic acids, by reducing intracellular reactive oxygen species (ROS) and modulating key metabolic regulators. ALVs upregulated SREBP-2, LDLR, and phosphorylated AMPK and Akt, leading to enhanced LDL and glucose uptake, while downregulating FASN and PPAR-γ, thereby reducing lipid accumulation. In mice fed a high-fat and high-fructose (HFHF) diet, ALV supplementation improved glucose tolerance and decreased total cholesterol, LDL, and hepatic injury biomarkers (ALT, AST, and LDH) without inducing toxicity. These findings demonstrate that ALVs exert hypocholesterolemic, hypoglycemic, and lipid-lowering effects through coordinated modulation of AMPK/Akt pathways. Overall, ALVs emerge as safe, multifunctional nanovesicles capable of counteracting oxidative stress and metabolic dysfunction, highlighting their potential as innovative bioactive ingredients for functional foods or nutraceutical formulations targeting metabolic syndrome. Full article

Lightweight metal matrix composites (MMCs) continue to attract significant interest due to their potential to deliver high mechanical performance at reduced weight, meeting the increasing demands of aerospace, automotive and advanced manufacturing sectors. Among these systems, aluminum-, magnesium- and titanium-based MMCs stand out for their favorable strength-to-weight ratios, corrosion resistance and versatility in processing. Although numerous studies have explored individual MMC families, the literature still lacks comparative reviews that integrate quantitative mechanical data with a broad evaluation of processing, microstructural control and application-driven performance. This review addresses these gaps by providing a comprehensive and data-driven assessment of lightweight MMCs. Recent advances in reinforcement strategies, hybrid architectures and processing routes—including friction stir processing, powder metallurgy and semi-solid techniques—are systematically examined. Emerging developments in syntactic metal foams and functionally gradient MMCs are analyzed in detail, along with practical considerations such as machinability, corrosion resistance, and high-temperature performance, integrated with AI/machine learning for predictive optimization. Overall, this work provides an integrated and critical perspective on the capabilities, limitations, and design trade-offs of lightweight MMCs, positioning them as sustainable and high-performance alternatives for extreme environments. By combining qualitative insights with quantitative meta-analyses and new experimental contributions, it offers a valuable reference for researchers and engineers seeking to optimize material selection and tailor the performance of MMCs for next-generation lightweight structures, surpassing previous reviews through holistic and innovation-driven insights. Full article

In response to the rapidly increasing volume of express parcels, the challenges of insufficient detection accuracy and slow response speed in express parcel quality detection systems have become prominent. To address these issues, we propose the YOLO11-FGA algorithm, an enhanced version of the YOLO11n model designed to improve both detection accuracy and response speed. The key innovation in this model is the introduction of the FasterNet backbone, which enhances the feature extraction capabilities while maintaining a lightweight design, thus improving overall computational efficiency. Additionally, we incorporate the C3k2_GhostDynamicConv module, which combines the GhostBottleneck and DynamicConv structures to significantly enhance the detection and feature extraction capabilities, particularly for subtle and complex packaging defects. To further improve detection performance, an Auxiliary training head (Aux) is added to the YOLO11 detection head, enhancing multi-scale feature fusion and boosting the accuracy of small target detection, which is essential for identifying minor defects. Experimental results demonstrate that YOLO11-FGA outperforms YOLO11n, with improvements in accuracy (1.1%), recall (1.3%), mAP@0.5 (2.3%), and mAP@0.5:0.95 (1.4%). These results highlight the superior performance of the YOLO11-FGA algorithm, which offers enhanced detection accuracy, robustness, and computational efficiency, making it a highly effective solution for real-time express parcel quality detection in logistics applications. Full article

Background and Clinical Significance: The paper investigates the use of the Histolog^® Scanner, a confocal microscopy–based device, as a potential tool for intraoperative neuropathological diagnosis of brain tumors. Traditional intraoperative diagnosis, relying on frozen sections and squash preparations, can introduce artifacts and consume valuable tissue. The Histolog^® Scanner offers a plug-and-play solution capable of acquiring high-resolution images of fresh tissue surfaces in minutes while preserving tissue for further histological or molecular analyses. Cases Presentation: Three clinical cases—two women and one-man, mean age 57.3 years—undergoing neurosurgery for distinct brain lesions were included. Tissue samples were immersed in fluorescent dye, rinsed, and immediately analyzed with the Histolog^® Scanner before standard intraoperative histopathology. In the first case, a glioblastoma wild-type, traditional methods struggled to define tumor margins, whereas the device provided rapid, detailed imaging to guide resection. In the second case, a meningioma, the scanner confirmed lesion identity quickly, eliminating the need for a cryostat and reducing artifacts. In the third case, a brain metastasis, integration with cytological apposition allowed simultaneous assessment of lesion margins and nature without freezing the tissue. Conclusions: The Histolog^® Scanner demonstrated multiple advantages: rapid intraoperative use, clear margin visualization, preservation of tissue for subsequent analyses, reduce unnecessary resection, thereby helping to lower the risk of recurrence. This device may complement standard intraoperative methods, enhancing diagnostic accuracy and influencing postoperative treatment planning. Overall, the Histolog^® Scanner represents an innovative tool combining speed, precision, and tissue preservation, suggesting a promising role in establishing a new standard for intraoperative neurosurgical diagnosis. Full article

Technological innovation and the efficiency of resource allocation in Chinese new energy vehicle enterprises represent critical factors influencing the sustainable development of the industry. By applying a two-stage dynamic network DEA model to analyze the comprehensive and stage-specific technological innovation efficiency of 13 A-share-listed new energy vehicle enterprises between 2017 and 2024, this study reveals that both overall and phase-specific innovation efficiencies remain below optimal levels. Moreover, the average technological R&D efficiency across these firms is found to be lower than their average achievement transformation efficiency, highlighting the urgent need to improve innovation performance in this sector. Grey relational analysis of influencing factors identifies six key determinants of technological innovation efficiency: the shareholding ratio of the largest shareholder, R&D investment intensity, the proportion of employees holding bachelor’s degrees or higher, management capability, return on equity, and total asset turnover. In comparison, government subsidies and total assets exhibit relatively limited influence on technological innovation efficiency. Full article