Search Results (11,652)

In recent years, experts have associated forecasts of global energy consumption with energy transitions. This paper presents the research results of the paths and trajectories of the global transformations of world energy, including demographic, technological, energy, transport, and communication changes. After demonstrating the long-term trends in global energy consumption, fossil and renewable energy sources, and nuclear energy using neuroforecasting methods, this study explains global demographic development and its relationship with global innovation and technological processes as explained by the flow of global patent applications. The relationship between energy transition and the previously mentioned two factors is also justified based on the trajectories developed by the neural network forecasting. By leveraging the fundamental laws of energy conservation, robust patterns in the evolution and development of global energy could be identified. It is demonstrated that mankind has entered the era of four closely interconnected global transitions: demographic, energy, technological, and political–economic, all at once. According to the results, civilizational changes are currently taking place in global energy advancement, indicating an energy transition to a new quality of energy development. The permanent growth patterns of the energy density of energy sources used and their impact on labor productivity and the speed of movement of people and goods in the economy are also discussed. Finally, the contour of future developments in energy technologies is determined. It is also forecast that future energy technologies are expected to be largely associated with the exploration of outer space, development of robotics, and the expansion of artificial intelligence capabilities. Full article

The increasing demand for high-throughput, real-time, and single-molecule protein analysis in precision medicine has propelled the development of novel sensing technologies. Among these, nanopore-based methods have garnered significant attention for their unique capabilities, including label-free detection, ultra-sensitivity, and the potential for miniaturization and portability. Originally designed for nucleic acid sequencing, nanopore technology is now being adapted for peptide and protein analysis, offering promising applications in biomarker discovery and disease diagnostics. This review examines the latest advances in biological, solid-state, and hybrid nanopores for protein sensing, focusing on their ability to detect amino acid sequences, structural variants, post-translational modifications, and dynamic protein–protein or protein–drug interactions. We critically compare these systems to conventional proteomic techniques, such as mass spectrometry and immunoassays, discussing advantages and persistent technical challenges, including translocation control and signal deconvolution. Particular emphasis is placed on recent advances in protein sequencing using biological and solid-state nanopores and the integration of machine learning and signal-processing algorithms that enhance the resolution and accuracy of protein identification. Nanopore protein sensing represents a disruptive innovation in biosensing, with the potential to revolutionize clinical diagnostics, therapeutic monitoring, and personalized healthcare. Full article

The Yunyao Aerospace Constellation Program is the core project being developed by Yunyao Aerospace Technology Co., Ltd., Tianjin, China. It aims to provide scientific data for weather forecasting, as well as research on the ionosphere and neutral atmosphere. It is expected to launch 90 high time resolution weather satellites. Currently, the Yunyao space constellation provides nearly 16,000 BDS, GPS, GLONASS, and Galileo multi-system occultation profile products on a daily basis. This study initially calculates the precise orbits of Yunyao LEO satellites independently using each GNSS constellation, allowing the derivation of the neutral atmospheric refractive index profile. The precision of the orbit product was evaluated by comparing carrier-phase residuals (ranging from 1.48 cm to 1.68 cm) and overlapping orbits. Specifically, for GPS-based POD, the average 3D overlap accuracy was 4.93 cm, while for BDS-based POD, the average 3D overlap accuracy was 5.18 cm. Simultaneously, the global distribution, the local time distribution, and penetration depth of the constellation were statistically analyzed. BDS demonstrates superior performance with 21,093 daily occultation profiles, significantly exceeding GPS and GLONASS by 15.9% and 121%, respectively. Its detection capability is evidenced by 79.75% of profiles penetrating below a 2 km altitude, outperforming both GPS (78.79%) and GLONASS (71.75%) during the 7-day analysis period (DOY 169–175, 2023). The refractive index profile product was also compared with the ECWMF ERA5 product. At 35 km, the standard deviation of atmospheric refractivity for BDS remains below 1%, while for GPS and GLONASS it is found at around 1.5%. BDS also outperforms GPS and GLONASS in terms of the standard deviation in the atmospheric refractive index. These results indicate that Yunyao satellites can provide high-quality occultation product services, like for weather forecasting. With the successful establishment of the global BDS-3 network, the space signal accuracy has been significantly enhanced, with BDS-3 achieving a Signal-in-Space Ranging Error (SISRE) of 0.4 m, outperforming GPS (0.6 m) and GLONASS (1.7 m). This enables superior full-link occultation products for BDS. Full article

There is currently no effective therapeutic capable of arresting or inducing regression of primary or metastatic brain cancers. This article presents both pre-clinical and clinical studies supportive that a new bioengineered technology could induce regression and/or elimination of primary and metastatic brain cancers through three disease-modifying mechanisms. Transcranial Radiofrequency Wave Treatment (TRFT) is non-thermal, non-invasive and self-administered in-home to safely provide radiofrequency waves to the entire human brain. Since TRFT has already been shown to stop and reverse the cognitive decline of Alzheimer’s Disease in small studies, evidence is provided that three key mechanisms of TRFT action, alone or in synergy, could effectively treat brain cancers: (1) enhancement of brain meningeal lymph flow to increase immune trafficking between the brain cancer and cervical lymph nodes, resulting in a robust immune attack on the brain cancer; (2) rebalancing of the immune system’s cytokines within the brain or brain cancer environment to decrease inflammation therein and thus make for an inhospitable environment for brain cancer growth; (3) direct anti-proliferation/antigrowth affects within the brain tumor microenvironment. Importantly, these mechanisms of TRFT action could be effective against both visualized brain tumors and those that are yet too small to be identified through brain imaging. The existing animal and human clinical evidence presented in this perspective article justifies TRFT to be clinically tested immediately against both primary and metastatic brain cancers as monotherapy or possibly in combination with immune checkpoint inhibitors. Full article

As global energy systems transition towards greater reliance on renewable energy sources, the integration of energy storage systems (ESSs) becomes increasingly critical to managing the intermittency and variability associated with renewable generation. This paper provides a comprehensive review of the application of evolutionary game theory (EGT) to optimize ESSs, emphasizing its role in enhancing decision-making processes, operation scheduling, and multi-agent coordination within dynamic, decentralized energy environments. A significant contribution of this paper is the incorporation of negotiation mechanisms and collaborative decision-making frameworks, which are essential for effective multi-agent coordination in complex systems. Unlike traditional game-theoretic models, EGT accounts for bounded rationality and strategic adaptation, offering a robust tool for modeling the interactions among stakeholders such as energy producers, consumers, and storage operators. The paper first addresses the key challenges in integrating ESS into modern power grids, particularly with high penetration of intermittent renewable energy. It then introduces the foundational principles of EGT and compares its advantages over classical game theory in capturing the evolving strategies of agents within these complex environments. A key innovation explored in this review is the hybridization of game-theoretic models, combining the stability of classical game theory with the adaptability of EGT, providing a comprehensive approach to resource allocation and coordination. Furthermore, this paper highlights the importance of deliberative democracy and process-based negotiation decision-making mechanisms in optimizing ESS operations, proposing a shift towards more inclusive, transparent, and consensus-driven decision-making. The review also examines several case studies where EGT has been successfully applied to optimize both local and large-scale ESSs, demonstrating its potential to enhance system efficiency, reduce operational costs, and improve reliability. Additionally, hybrid models incorporating evolutionary algorithms and particle swarm optimization have shown superior performance compared to traditional methods. The future directions for EGT in ESS optimization are discussed, emphasizing the integration of artificial intelligence, quantum computing, and blockchain technologies to address current challenges such as data scarcity, computational complexity, and scalability. These interdisciplinary innovations are expected to drive the development of more resilient, efficient, and flexible energy systems capable of supporting a decarbonized energy future. Full article

With the advancement of precision agriculture, Unmanned Aerial Vehicle (UAV)-based remote sensing has been increasingly employed for monitoring crop water and nutrient status due to its high flexibility, fine spatial resolution, and rapid data acquisition capabilities. This review systematically examines recent research progress and key technological pathways in UAV-based remote sensing for crop water and nutrient monitoring. It provides an in-depth analysis of UAV platforms, sensor configurations, and their suitability across diverse agricultural applications. The review also highlights critical data processing steps—including radiometric correction, image stitching, segmentation, and data fusion—and compares three major modeling approaches for parameter inversion: vegetation index-based, data-driven, and physically based methods. Representative application cases across various crops and spatiotemporal scales are summarized. Furthermore, the review explores factors affecting monitoring performance, such as crop growth stages, spatial resolution, illumination and meteorological conditions, and model generalization. Despite significant advancements, current limitations include insufficient sensor versatility, labor-intensive data processing chains, and limited model scalability. Finally, the review outlines future directions, including the integration of edge intelligence, hybrid physical–data modeling, and multi-source, three-dimensional collaborative sensing. This work aims to provide theoretical insights and technical support for advancing UAV-based remote sensing in precision agriculture. Full article

The purpose of the current research study was to identify appropriate educators for teaching entrepreneurship at the university level and to explore the best teaching methods for developing entrepreneurial knowledge and skills among students. The study aims to address two key questions in entrepreneurship education: (1) Who should teach entrepreneurship in universities? and (2) What methods are effective in teaching entrepreneurship in universities? The study was conducted using an interpretative phenomenological qualitative research approach. Data were collected from a purposive sample of eight (8) entrepreneurship educators from a South African university. Data collection spanned three months, from November 2024 to January 2025. The key findings of the study suggest that entrepreneurship should be taught by academics with practical experience, academics with at least a Master’s degree, entrepreneurs invited as guest lecturers, incubator professionals, and technology professionals. Additionally, the research revealed teaching methods that can be used to effectively teach entrepreneurship in universities: Universities need to prioritise hiring and training entrepreneurship educators with both academic and real-world experience and facilitate collaborations with incubators and real-world entrepreneurs. Teaching methods need to incorporate experiential learning methods such as startup simulations, case studies, and partnerships with innovation hubs. The study offers valuable insights into who should teach entrepreneurship and how it should be taught, emphasising the need for a multidisciplinary approach and practical orientation to develop entrepreneurial capabilities and mindsets among students. Full article

Vehicle detection plays a pivotal role in traffic management as a key technology for intelligent traffic management and driverless driving. However, current deep learning-based vehicle detection models face several challenges in practical applications. These include slow detection speeds, large computational and parametric quantities, high leakage and misdetection rates in target-intensive environments, and difficulties in deploying them on edge devices with limited computing power and memory. To address these issues, this paper proposes an improved vehicle detection method called SMA-YOLO, based on the YOLOv7 model. Firstly, MobileNetV3 is adopted as the new backbone network to lighten the model. Secondly, the SimAM attention mechanism is incorporated to suppress background interference and enhance small-target detection capability. Additionally, the ACON activation function is substituted for the original SiLU activation function in the YOLOv7 model to improve detection accuracy. Lastly, SIoU is used to replace CIoU to optimize the loss of function and accelerate model convergence. Experiments on the UA-DETRAC dataset demonstrate that the proposed SMA-YOLO model achieves a lightweight effect, significantly reducing model size, computational requirements, and the number of parameters. It not only greatly improves detection speed but also maintains higher detection accuracy. This provides a feasible solution for deploying a vehicle detection model on embedded devices for real-time detection. Full article

Sign language serves as a vital way to communicate with individuals with hearing loss, deafness, or a speech disorder, yet accessibility remains limited, requiring technological advances to bridge the gap. This study presents the first real-time Greek Sign Language recognition system utilizing deep learning and embedded computers. The recognition system is implemented using You Only Look Once (YOLO11X-seg), an advanced object detection model, which is embedded in a Python-based framework. The model is trained to recognize Greek Sign Language letters and an expandable set of specific words, i.e., the model is capable of distinguishing between static hand shapes (letters) and dynamic gestures (words). The most important advantage of the proposed system is its mobility and scalable processing power. The data are recorded using a mobile IP camera (based on Raspberry Pi 4) via a Motion-Joint Photographic Experts Group (MJPEG) Stream. The image is transmitted over a private ZeroTier network to a remote powerful computer capable of quickly processing large sign language models, employing Moonlight streaming technology. Smaller models can run on an embedded computer. The experimental evaluation shows excellent 99.07% recognition accuracy, while real-time operation is supported, with the image frames processed in 42.7 ms (23.4 frames/s), offering remote accessibility without requiring a direct connection to the processing unit. Full article

The SARS-CoV-2 spike (S1) protein facilitates viral entry through binding to angiotensin-converting enzyme 2 (ACE2), but it also contains an Arg–Gly–Asp (RGD) motif that may enable interactions with RGD-binding integrins on ACE2-negative cells. Here, we provide quantitative evidence for this alternative binding pathway using a live-cell, label-free resonant waveguide grating (RWG) biosensor. RWG technology allowed us to monitor real-time adhesion kinetics of live cells to RGD-displaying substrates, as well as cell adhesion to S1-coated surfaces. To characterize the strength of the integrin–S1 interaction, we determined the dissociation constant using two complementary approaches. First, we performed a live-cell competitive binding assay on RGD-displaying surfaces, where varying concentrations of soluble S1 were added to cell suspensions. Second, we recorded the adhesion kinetics of cells on S1-coated surfaces and fitted the data using a kinetic model based on coupled ordinary differential equations. By comparing the results from both methods, we estimate that approximately 33% of the S1 molecules immobilized on the Nb₂O₅ biosensor surface are capable of initiating integrin-mediated adhesion. These findings support the existence of an alternative integrin-dependent entry route for SARS-CoV-2 and highlight the effectiveness of label-free RWG biosensing for quantitatively probing virus–host interactions under physiologically relevant conditions without the need of the isolation of the interaction partners from the cells. Full article

This paper proposes and evaluates a novel real-time cybersecurity framework integrating artificial intelligence (AI) and blockchain technology to enhance the detection and auditability of cyber threats. Traditional cybersecurity approaches often lack transparency and robustness in logging and verifying AI-generated decisions, hindering forensic investigations and regulatory compliance. To address these challenges, we developed an integrated solution combining a convolutional neural network (CNN)-based anomaly detection module with a permissioned Ethereum blockchain to securely log and immutably store AI-generated alerts and relevant metadata. The proposed system employs smart contracts to automatically validate AI alerts and ensure data integrity and transparency, significantly enhancing auditability and forensic analysis capabilities. To rigorously test and validate our solution, we conducted comprehensive experiments using the CICIDS2017 dataset and evaluated the system’s detection accuracy, precision, recall, and real-time responsiveness. Additionally, we performed penetration testing and security assessments to verify system resilience against common cybersecurity threats. Results demonstrate that our AI-blockchain integrated solution achieves superior detection performance while ensuring real-time logging, transparency, and auditability. The integration significantly strengthens system robustness, reduces false positives, and provides clear benefits for cybersecurity management, especially in regulated environments. This paper concludes by outlining potential avenues for future research, particularly extending blockchain scalability, privacy enhancements, and optimizing performance for high-throughput cybersecurity applications. Full article

The construction industry, a cornerstone of global economic growth, continues to struggle with entrenched inefficiencies, including low productivity, cost overruns, and fragmented project delivery. Addressing these persistent challenges requires more than incremental improvements, it demands a strategic unification of Lean Construction, Building Information Modeling (BIM), and Emerging Technologies. This systematic review synthesizes evidence from 64 academic studies to identify the most influential tools, techniques, and methodologies across these domains, revealing both their individual strengths and untapped synergies. The analysis highlights widely adopted Lean practices such as the Last Planner System (LPS) and Just-In-Time (JIT); BIM capabilities across 3D, 4D, 5D, 6D, and 7D dimensions; and a spectrum of digital innovations including Digital Twins, AR/VR/MR, AI, IoT, robotics, and blockchain. Crucially, the review demonstrates that despite rapid advancements, integration remains sporadic and unstructured, representing a critical research and industry gap. By moving beyond descriptive mapping, this study establishes an essential foundation for the development of robust, adaptable integration frameworks capable of bridging theory and practice. Such frameworks are urgently needed to optimize efficiency, enhance sustainability, and enable innovation in large-scale and complex construction projects, positioning this work as both a scholarly contribution and a practical roadmap for future research and implementation. Full article

Connected Autonomous Vehicles (CAVs) technology holds immense potential for enhancing traffic safety and efficiency; however, its inherent complexity presents significant challenges for conventional autonomous driving. World Models (WMs), an advanced deep learning paradigm, offer an innovative approach to address these CAV challenges by learning environmental dynamics and precisely predicting future states. This survey systematically reviews the advancements of WMs in connected automated driving, delving into the key methodologies and technological breakthroughs across six core application domains: cooperative perception, prediction, decision-making, control, human–machine collaboration, and scene generation. Furthermore, this paper critically analyzes the current limitations of WMs in CAV scenarios, particularly concerning multi-source heterogeneous data fusion, physical law mapping, long-term temporal memory, and cross-scenario generalization capabilities. Building upon this analysis, we prospectively outline future research directions aimed at fostering the development of more robust, efficient, and interpretable WMs. Ultimately, this work aims to provide a crucial reference for constructing safe, efficient, and sustainable connected automated driving systems. Full article

This study explores the relationship between digital human resource management (HRM) practices, organizational culture, and employees’ perceived digital competence within Greek organizations. While digitalization has become a central priority in human resource management (HRM), there is still limited understanding of how cultural context shapes the effectiveness of digital HR interventions. Using a quantitative approach, data were collected via an online questionnaire from 257 employees across various sectors. The research employed the method of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-Group Analysis (MGA) to examine the structural relationships between digital HRM practices—such as e-learning, onboarding, and performance management—and digital competence, taking into account different organizational culture profiles. The results show that digital HRM practices have a positive, but modest, impact on employees’ digital skills, with e-learning emerging as the most influential factor. Importantly, the effect of HRM practices varies significantly according to the cultural environment: supportive and innovative cultures foster stronger development of digital competence compared to hierarchical settings. The findings underline the necessity for organizations to adapt digital HR strategies to their specific cultural context and not to rely solely on technological solutions. This research contributes to the growing literature by demonstrating the interplay between technology and culture in shaping employees’ digital capabilities and suggests that a balanced focus on both is essential for successful digital transformation. Full article

With sub-millimeter deformation capture capability, InSAR technology has become an important tool for surface deformation monitoring. However, it is still limited by interferences like land subsidence and bridge deformation in long-term linear engineering monitoring, failing to accurately identify track deformation. Based on RadarSAT-2 and Sentinel-1A satellite data from 2013 to 2023, this study uses time-series InSAR technology (PS-InSAR) to accurately invert the track deformation information of the Beijing–Tianjin Intercity Railway (Beijing section) in the past decade. Key findings demonstrate (1) rigorous groundwater policies (extraction bans and artificial recharge) drove up to 48% regional subsidence mitigation in Chaoyang–Tongzhou, with synchronous track deformation exhibiting 0.6‰ spatial gradient; (2) critical differential subsidence identified at DK11–DK23, where maximum annual settlement decreased from 110 to 49.7 mm; (3) XGBoost-SHAP modeling revealed dynamic driver shifts: confined aquifer depletion dominated in 2015 (>60%), transitioned to delayed consolidation in 2018 (45%), and culminated in phreatic recovery–compressible layer coupling by 2022 (55%). External factors (tectonic/urban loads) played secondary roles. The rise in groundwater levels induces soil dilatancy, while the residual deformation in cohesive soils—exhibiting hysteresis relative to groundwater fluctuations—manifests as surface subsidence deceleration rather than rebound. This study provides a scientific basis for in-depth understanding of the differential subsidence mechanism along high-speed railways and disaster prevention and control. Full article