Search Results (2,434)

As power densities in Wide Band Gap (WBG) semiconductors increase, traditional steady-state cooling methods often fail to address the rapid thermal transients. This paper investigates a diversity of cooling architectures for discrete Silicon Carbide (SiC) MOSFETs, and a frequency-dependent thermal characterization of different cooling technologies. This paper also presents a new measurement method of heat transfer through multilayer systems, which determines the thermal diffusivity of the power device in real dynamic operation. The measurement of the stored energy in the active component using temperature sensors proves itself to be a practical, rapid, low-cost, and non-invasive method. The results show that the active Peltier stage significantly allows for more frequent current bursts before reaching the thermal limit, thereby proving the system’s capability in high-frequency transient applications to be thermally stable. The analysis of different power systems with different types of cooling, for a range of frequencies of the drive signal for SiC MOSFETs, enables power electronic engineers to evaluate the efficacy of the cooling systems related to the operating frequency and not only as a function of the dissipated power. Analysis of thermal faults in MOSFETs with SEM/EDS methods reveals the weak points in the design of these WBG devices. Full article

Blockchain is one important building blocks of the Internet of the future, called Web3. The Blockchain technology supports a wide range of applications, spanning from Smart Cities and automotive industries, from agriculture to energy. The healthcare sector, in particular, has experienced a profound impact from blockchain-based technologies, paving the way for the development of true digital healthcare systems. By enabling secure and immutable data storage, and facilitating the sharing of this information among all nodes possessing a local copy of the distributed ledger, blockchain plays a vital role in the analysis of healthcare data. This paper provides a comprehensive survey of the main blockchain platforms utilized in the digital healthcare, integrated with a comparative analysis. In addition, the implementation of Innovative permissioned Blockchain for eHealth (IBEH) is presented and discussed in detail. IBEH addresses key challenges in digital health data management, including secure and controlled access to sensitive health information, ensuring data integrity and traceability, and secure sharing between different healthcare institutions and organizations. This is made possible by decoupling the application and blockchain layers and by a flexible, customizable, and easily deployable infrastructure. IBEH integrates the application-oriented and embedded layer with that of a blockchain network built with the MultiChain platform, which uses smart contracts with permissions, REST APIs, and RPC calls. The main features and its associated smart contracts within the healthcare domain are discussed. Finally, the analysis of performance is provided. Full article

The construction sector plays a significant role in global energy consumption and carbon emissions; improving the energy efficiency of existing buildings a fundamental requirement for sustainable development. Retrofitting the internal walls of buildings requires insulating materials that not only provide effective thermal performance but are also environmentally friendly, low-carbon, and compatible with circular-economy principles. In this context, recycled textile materials, especially cotton and polyester, are gaining increasing attention as sustainable insulation systems. This review article aims to critically evaluate the thermal insulation, environmental performance, and potential use of woven textile insulation structures based on recycled cotton, recycled polyester, and an equal combination of both in internal wall retrofitting. This article systematically reviews the available scientific literature and presents a conceptual framework based on multilayer woven structures. This review highlights that increasing the number of layers can play a significant role in improving thermal resistance, reducing heat transfer, and limiting internal energy loss by increasing the static air spaces between the fibers. Furthermore, the use of recycled textiles can also achieve environmental benefits such as reducing textile waste, conserving natural resources, and reducing overall carbon emissions. The research also offers a useful guiding framework for the development of sustainable building technologies based on low-carbon construction, efficient use of resources, and a circular economy. Full article

With the continuous advancement of deep and ultra-deep well drilling technologies, formations with complex pressure windows are becoming increasingly common. During casing running operations, it is necessary to ensure both leak prevention in loss-prone formations and pressure stabilization in high-pressure formations, demanding increasingly higher accuracy in wellbore pressure calculation. This study establishes a wellbore pressure calculation model for managed pressure casing (MPC) running in deep wells, specifically addressing the scenario where a multi-density gradient drilling fluid column exists in the annulus after tripping out. The model’s novelty lies in integrating transient surge pressure calculation with a dynamic fluid column structure model that tracks the displacement of multi-density drilling fluid layers during casing running. The governing equations based on one-dimensional unsteady flow theory are solved using the method of characteristics with adaptive time stepping and a grid independence study confirming the discretization scheme. Quantitative analysis reveals that casing running speed is the dominant factor affecting surge pressure; when the speed increases from 0.5 m/s to 1.5 m/s, the surge pressure increases from approximately 1.2 MPa to 3.5 MPa at a 2000 m depth. Drilling fluid properties also significantly influence surge pressure: increasing the density from 2.0 g/cm³ to 2.22 g/cm³ results in a surge pressure increase of approximately 0.6 MPa; increasing the yield value from 2.85 Pa to 15 Pa leads to an increase of about 1.1 MPa; the surge pressure shows a clear increasing trend with both the consistency coefficient and flow behavior index. Casing running depth affects the buffering effect of the bottomhole flow channel; when the casing is run to 7000 m, the surge pressure is approximately 0.5 MPa higher than at 2000 m. Taking a typical deep well (8578 m) with a negative pressure window of −0.008 g/cm³ as an example, three casing running speed plans were designed and evaluated. Plan 1 was selected with running speeds ranging from 0.16 m/s in the upper section to 0.115 m/s in the lower section, maintaining the equivalent circulating density (ECD) within the safe density window throughout the entire operation. Field application of this plan proceeded smoothly without any occurrences of lost circulation or overflow. This provides a practical basis for MPC running technology in deep wells with narrow or negative pressure windows. Full article

Non-mechanical beam steering technology based on liquid crystal optical switches and liquid crystal polarization gratings holds significant application prospects in fields such as laser communication, radar detection, and optical information processing. Traditional nematic liquid crystal optical switches exhibit slow response speeds, whereas novel ferroelectric liquid crystal optical switches, despite their fast response, are hampered in engineering applications by complex fabrication processes, the large number of devices required for cascading, and substantial module thickness. To address these issues, this paper proposes and demonstrates a fast non-mechanical beam steering scheme by cascading a stressed polymer network liquid crystal (SPNLC) optical switch with a liquid crystal polarization grating. The SPNLC is fabricated by mechanically shearing a polymerized liquid crystal–polymer composite, enabling sub-millisecond response and continuous linear phase modulation without the need for an alignment layer. A 30-μm-thick SPNLC half-wave plate was prepared, which introduces a phase retardation of 3.6 μm under a driving voltage of 300 V, and the rise time and fall time are measured to be approximately 526 μs and 560 μs at a driving voltage of 20 V with a 1 kHz square wave, and 470 μs and 538 μs at 27 V under the same waveform conditions. Cascaded with a passive polarization grating, the waveplate enables fast electrical switching of the beam between the ±1st diffraction orders. Furthermore, a two-dimensional multi-angle beam deflector was constructed based on a supra-binary cascade scheme. Experimental results confirm that the system possesses sub-millisecond response, large phase retardation, continuous tunability, and an alignment-layer-free fabrication process, demonstrating its feasibility for large-range fast beam scanning. Full article

Heavy and extra-heavy oils represent a large and growing share of recoverable hydrocarbon resources, yet their extreme viscosity, high heteroatom content, and non-Newtonian behavior routinely defeat empirical correlations developed for conventional crude. Machine learning has emerged as a candidate response to this modeling gap, but existing reviews largely catalog applications without asking whether the technology is actually ready for industrial deployment. This critical review synthesizes machine learning applications across five thematic domains of the heavy-oil value chain: physicochemical property prediction, enhanced oil recovery, flow assurance, reactive recovery, and downstream upgrading. Studies are read through a three-phase historical lens, tracing the field’s progression from empirical-correlation replacement to methodological diversification to physics-informed and closed-loop integration, and evaluated against a Technology Readiness Level (TRL) scale adapted specifically for heavy-oil machine learning. The multilayer perceptron anchors more of the primary corpus than any other architecture, a pattern that, in our interpretation, reflects small-sample, low-dimensional regression needs rather than any demonstrated advantage over other architectures. Enhanced oil recovery is the only cluster to reach organizational-scale deployment, anchored by a single multi-decade operator program, Chevron’s San Joaquin Valley i-field; the remaining clusters are constrained less by modeling sophistication than by single-basin datasets and undisclosed uncertainty. Measured against three falsifiable deployability criteria, fidelity preservation below 10° API, operator-grade interpretability, and demonstrated laboratory-to-field transferability, no study in the reviewed corpus is documented to satisfy all three simultaneously; because industrial implementations are frequently proprietary, this is a statement about the published record identified by this search, not a claim that the capability does not exist. Federated learning, physics-informed architectures, and sequence-aware models emerge as the directions most likely to close this gap. Full article

Plastic is a valuable material for packaging of food and pharmaceuticals, protective wrappings in construction and agriculture, and fluid storage. Flexible plastic or plastic film waste from packaging, agriculture, and construction applications grows at a rate of at least 92 million metric tons a year, is considered challenging to recycle, and is typically landfilled. In recent years, there have been great advancements in plastic recycling technology in order to deal with the global challenge of plastic waste buildup and support legislation from a local to national level to implement recycling. This work highlights the most recent advancements in plastic film recycling. Plastic films are mono- or multilayered based on what their applications will be, with multilayer multimaterial films being the more challenging feedstock for recycling. Mechanical recycling cannot easily process flexible films. Pyrolysis can use polyolefin-based film as feedstock but is not practiced at scale to match the rate of plastic film waste generation, and incineration can recover energy from film feedstock but is not recycling plastic. This has motivated the development of new recycling technologies designed around plastic films. Better characterization technologies to identify film compositions in municipal waste streams have been key to sorting out film feedstock for mechanical recycling and the baling of flexible plastic waste, but they struggle with multilayer films and black plastic. Compatibilization enables the recycling of mixed plastic waste but requires polymer compositions for selecting specific compatibilizers. Dissolution–precipitation recovers individual types of polymers from multilayer films and, at the same time, can purify polymers from additives or contaminants, but requires intense solvent processing and associated energy. Delamination of multilayer films can separate and recover solid films of polyolefins at relatively low amounts of solvent but requires quality feedstock to be efficient. Both dissolution–precipitation and delamination recycling of films recover the original polymer molecules and maintain their embodied energy, hence support circularity. In the case of PET-containing films, depolymerization to recover PET monomers offers opportunities to recycle challenging film feedstock. Full article

Hybrid solar–thermal energy harvesting systems are an important advancement in renewable energy technology, enabling simultaneous production of electrical power and useful thermal energy within a single compact platform. However, their performance is often limited by poor heat transfer and inefficient mass transport of working fluids, leading to photovoltaic thermal degradation and significant exergy losses. This study aims to optimize heat and mass transport processes in a hybrid solar–thermal mechanical system to enhance energy recovery and ensure long-term operational reliability. A three-dimensional numerical model based on the finite volume method (FVM) was developed using the governing equations of continuity, momentum, and energy conservation. A Multi-Objective Genetic Algorithm (MOGA) was employed to determine optimal microchannel geometries by analyzing variable cross-section effects on flow behavior and thermal boundary layer disruption. At the Reynolds number of 2000, the optimized configuration increases the average Nusselt number by 43.5% compared to a smooth channel. Consequently, the photovoltaic operating temperature decreases by 12.6 °C, improving electrical efficiency by 9.3%. The system achieves a maximum thermal efficiency and net energy gain of 76.8%, while maintaining an acceptable 16.3% increase in pumping power. The results confirm that optimizing mass transport is essential for effective thermal regulation and improved energy conversion performance, providing a strong foundation for high-efficiency solar collector design. Full article

The transition to a new industrial paradigm beyond Industry 5.0 demands hyperconnectivity and massive processing, creating a paradox where high computational demand can threaten corporate sustainability goals (ESG). This study aims to structure and prioritize a Multilayer Framework of multicloud technologies to balance disruptive advances, lean optimization, and decarbonization. A mixed, sequential, and exploratory-normative approach was used. Initially, the literature was triangulated with expert panels and suppliers to map 21 technological functionalities, structuring them into four layers of a bidirectional value flow. Then, a hybrid multi-criteria modeling (AHP-TOPSIS) was applied to rank these technologies against five market constraints. Calibration with AHP revealed that Cyber Resilience, approximately 38%, and Process Optimization, approximately 27%, lead executive priorities, surpassing environmental impact or cost efficiency. As a result, the TOPSIS ranking highlighted Human–Machine Symbiosis (BCI/neuroergonomic readiness), Zero Trust architecture, Federated Learning, and GenAI KPI Analytics as the leading functionalities in their respective layers, with Hyper-BPM emerging as a closely associated optimization engine at the governance layer. Finally, the proposed roadmap was assessed using an anonymized industrial Proof of Concept (PoC) in a brownfield advanced manufacturing facility, providing evidence of its operational feasibility for integrating lean optimization with legacy systems and ESG-oriented monitoring. It is concluded that industrial sustainability does not rely solely on green technologies, but on decentralized orchestration along the Edge-Cloud continuum. Environmental gains are therefore more likely to emerge when cyber governance and lean-oriented operational management jointly support decentralized multicloud orchestration. Full article

The surface of nodular cast iron is susceptible to wear failure under high-load conditions. In this study, Fe-Cr-Ni wear-resistant coatings were developed on the surface of nodular cast iron using laser cladding technology. The influence of laser power (P), powder feeding rate (F), scanning speed (V), and overlapping rate (φ) on the microhardness and dilution rate of the coatings was analyzed by response surface methodology, while the model’s accuracy was evaluated through analysis of variance. Subsequently, the multi-objective particle swarm optimization algorithm was utilized to identify the optimal process parameters (P = 1350 W, V = 12.5 mm/s, F = 9 g/min, and φ = 45%) based on non-destructive testing results. The predictive model values closely matched the experimental results. The average microhardness of the Fe-Cr-Ni cladding layer was 620.3 HV, which was 2.8 times that of the nodular cast iron substrate. Importantly, the laser cladding layer demonstrated a significant improvement in wear resistance compared to the substrate. The wear mechanisms for the coating predominantly involved mild abrasive wear and adhesive wear, while the substrate primarily experienced severe adhesive wear. This study offers valuable insights for optimizing laser cladding process parameters for nodular cast iron. Full article

The latest generation of 4D imaging radar demonstrates significant potential in autonomous driving environmental perception, leveraging its capability to provide target elevation data and dense point clouds. This paper introduces a complete method for constructing a multimodal 4D imaging radar dataset for three-dimensional traffic scenes. It illustrates the hardware and software configurations of the data-acquisition vehicle. Methods including multi-sensor coordination, parameter calibration, timestamp synchronization and spatial datum synchronization are proposed. And eight typical three-dimensional traffic scenarios are designed, such as rainy weather environments, dense heterogeneous targets, enclosed tunnels, high-speed cut-in of multiple vehicles, multi-layered stereoscopic structures and edge working condition reproduction. In addition, this paper puts forward a frame-by-frame processing method for high-resolution images and point cloud data collected by the high-definition camera-LiDAR-4D imaging radar collaborative system. A large model-based 3D annotation method for multiple types of targets is proposed, generating a spatio-temporal sequence-optimized four-dimensional annotation sequence, and finally constructs a complete and high-quality multimodal 4D imaging radar dataset for three-dimensional traffic scenes. The results show that the constructed dataset enables the synchronization of timestamps and spatial coordinate systems. The large model can achieve high-precision 3D annotation for the four predefined target types. The dataset contains 11,400 frames of data from high-definition cameras, LiDAR, and 4D imaging radar, with 131,642 labels. This study will provide reliable fundamental support for the training and verification of 4D imaging radar perception algorithms, vehicle decision-making and planning in complex scenarios, and multi-sensor fusion technologies. Full article

Vanadium dioxide (VO₂), a typical thermochromic material, undergoes a metal–semiconductor transition (MST) near 68 °C, accompanied by lattice distortion and band structure reconstruction, making it an ideal material for smart window applications. However, VO₂ faces several bottlenecks that limit its engineering applications, including a relatively high phase transition temperature, poor color comfort, trade-offs between visible light transmittance and solar modulation efficiency, and inadequate long-term stability. This paper first discusses the structural evolution and mechanisms of the VO₂ phase transition. It then focuses on the three core performance aspects of VO₂-based smart windows: phase transition temperature regulation, color regulation, and optical performance regulation. Furthermore, it systematically reviews the latest research advancements in key technologies, including elemental doping, interfacial strain engineering, micro- and nanostructure engineering, multilayer film design, and inorganic–organic composite modification. Finally, the paper analyzes current challenges in terms of long-term stability, low-temperature flexible fabrication, skin comfort, and environmental friendliness, and discusses optimization pathways and future prospects for the practical application of VO₂-based smart windows. Full article

High-risk industries (HRI) face persistent safety challenges due to complex environments and multi-factor risks. Traditional manual monitoring is inefficient and reactive. While computer vision (CV) and deep learning (DL) have enabled automated risk perception, existing research lacks systematic review of the transition toward risk cognition. This systematic review, following PRISMA 2020 guidelines, searched Web of Science, Scopus, and IEEE Xplore for studies published from January 2021 to December 2025. After two-stage screening, 108 eligible studies were included. These studies span construction, mining, oil and gas, petrochemical, rail, energy, and maritime industries, with perception-layer applications predominating while cognition and early warning layer studies remain limited. We propose a “perception–cognition–early warning” framework to map the evolution from data to information, knowledge, and actionable decisions. Our findings reveal that visual perception technologies (e.g., Personal Protective Equipment (PPE) detection, object tracking) have matured, but significant bottlenecks persist in multimodal information fusion, knowledge reasoning, and decision-making. Achieving a true cognitive leap requires multimodal semantic alignment, scene graph construction, and causal inference. The proposed framework enables practitioners to design cognitive safety vision systems with scene understanding and interpretable decision-making capabilities. Key implementation strategies include addressing challenges in few-shot learning, cross-domain generalization, and human–machine collaboration. By integrating AI-driven perception, cognitive reasoning, and proactive intervention, this review supports the United Nations Sustainable Development Goals. Relevant goals include Goal 3 (health and well-being), Goal 8 (decent work), and Goal 9 (industry and innovation). Full article

China–Iraq international energy cooperation on natural gas constitutes a critical component of energy diplomacy under the Belt and Road Initiative. This study develops a multidimensional analytical framework encompassing geopolitical, economic–market, legal–policy, resource–technology, social–environmental, and bilateral–institutional dimensions. Subsequently, an integrated fuzzy DEMATEL-ISM model is employed to investigate the hierarchical structure and transmission pathways of influence among these factors. The findings reveal that the multiple factors can be classified into four clusters (driving, linkage, independent, and dependent), each exhibiting distinct roles in system evolution. Meanwhile, the model identifies a six-tier hierarchical structure, with directed pathways transmitting from deep-rooted factors through intermediate nodes to surface-level outcomes. Surface-level factors occupy the upper tier and directly shape cooperative performance, while intermediate-level factors act as transmission conduits that relay and transform deeper influences. Deep-level factors, including great-power rivalry, resource endowment, and market demand, form the system’s foundational layer. They remain immune to influence from upper tiers and thus require strategic governance to fundamentally ensure enduring cooperation sustainability. Consequently, policy priorities should center on deep-level drivers, complemented by targeted adjustments to intermediate and surface factors. This study offers a novel multi-level structural analytical lens and provides actionable policy recommendations to enhance cooperative resilience under the Belt and Road framework. Full article

In the mining process of shallow-buried and close-distance coal seam groups in western China, the interconnected collapse fractures between the overlying goaf and the surface form large-area composite goafs, which aggravate surface air leakage and elevate oxygen levels within the goaf. This, in turn, leads to hazardous conditions such as CO over-limits and O₂ deficiency at the working face’s return air corner, which seriously threatens the respiratory health of underground operators and the safe production of mines. Taking the 104 working face of a coal mine in Shenfu-Dongsheng Mining Area as the engineering background, this paper comprehensively adopts SF₆ tracer gas test, fuzzy cluster analysis, and CFD numerical simulation methods to systematically study the distribution characteristics of three-dimensional air leakage channels in composite goafs and their influence mechanism on gas migration in goafs, and proposes a dynamic pressure equalization ventilation (PEV) regulation technology system. The research results show that a multi-dimensional three-dimensional air leakage channel of “surface-interlayer-own layer-roadway” exists in the research area, in which the surface fracture air leakage velocity is about 0.068 m/s, and the interlayer and internal goaf air leakage velocity is about 0.384 m/s. The atmospheric pressure difference between the working face and the surface is the main controlling factor inducing the O₂ deficiency disaster of the working face. Every 100 Pa change in atmospheric pressure difference causes an O₂ concentration fluctuation of about 0.30% at the return air corner, and the critical pressure difference for activating PEV is determined to be 300 Pa. Setting the PEV regulation point at the return air outlet of the working face and adopting the combined dynamic regulation system of fans and air windows can realize accurate pressure balance between the working face and the overlying composite goaf. Full article