In this study, an extensive literature review has been conducted on the material characterization of UHPC and its potential for large-scale field applicability. The successful production of ultra-high performance concrete (UHPC) depends on its material ingredients and mixture proportioning, which leads to denser and relatively more homogenous particle packing. A database was compiled from various research and field studies around the world on the mechanical and durability performance of UHPC. It is shown that UHPC provides a viable and long-term solution for improved sustainable construction owing to its ultra-high strength properties, improved fatigue behavior and very low porosity, leading to excellent resistance against aggressive environments. The literature review revealed that the curing regimes and fiber dosage are the main factors that control the mechanical and durability properties of UHPC. Currently, the applications of UHPC in construction are very limited due to its higher initial cost, lack of contractor experience and the absence of widely accepted design provisions. However, sustained research progress in producing UHPC using locally available materials under normal curing conditions should reduce its material cost. Current challenges regarding the implementation of UHPC in full-scale structures are highlighted. This study strives to assist engineers, consultants, contractors and other construction industry stakeholders to better understand the unique characteristics and capabilities of UHPC, which should demystify this resilient and sustainable construction material.
The performance of the Bangladesh Atomic Energy Commission (BAEC) TRIGA Research Reactor (BTRR) was significantly improved by addressing signal integrity issues related to the NLW-1000 logarithmic power channel. The PA-1000 signal, which suffered from strength loss due to the unavoidable transmission line distance, was restored by installing a line driver between the PA-1000 and NLW-1000. Therefore, reactor power and period are visualized in the console in logarithmic scale from the well-acquisition of both pulse count and current signals. By addressing another wiring anomaly, the in activeness of the optocouplers in the FC-ISO-D and FC-ISO-C was resolved, a prestart checklist was performed, and it was found that the apparent “count rate low” failure arose from a mis-specified HMI message condition while the core logic correctly passed the test so further corrective action is needed for the HMI message. Presently, the BTRR functions up to 3MWth and the power is displayed in the CSC monitor. According to the user demand, the signal integrity is sufficiently enhanced and there is no error within the DACS & CSC. Intermediate circuitry between the PA-1000 and logarithmic module can be omitted in the upcoming advanced logarithmic power monitoring channel to get better performance. The present study underscores the efficacy of signal enhancement methodologies in elevating reactor functionality and delineates a trajectory for sustained progress in nuclear research infrastructure.
Renewable energy sources, Energy industries. Energy policy. Fuel trade
The increasing concerns about the environmental impact of fossil fuels have emphasized the importance of clean solar energy, which offers a pollution-free alternative for meeting growing energy needs. However, the accurate prediction of solar photovoltaic (SPV) based power generation is a very challenging task because of its inherent variability and uncertainty. To address this challenging problem, this paper applies several machine-learning, deep-learning, and their hybrid models such as: One-Dimensional Convolutional Neural Network (1D CNN), Bi-Directional Long Short-Term Memory (Bi-LSTM), Stacked LSTM, Artificial Neural Network (ANN), Linear Regression (LR), Support Vector Regression (SVR), XGBoost, and a hybrid CNN-LSTM model. These models are examined and compared on four different data sequences of DKASC Alice Springs dataset. The prediction performances of all these models are evaluated based on various error metrics: MAE (mean absolute error), explained variance, RMSE (root mean square error), R², and sMAPE (symmetric mean absolute percentage error). The simulation results demonstrates that Stacked LSTM model outperforms all other benchmark forecasting models and able to obtains average values of performance metrics i.e. MAE of 1.1157, RMSE of 2.3408, an Explained Variance of 0.8998, R² of 0.9004, and sMAPE of 1.1795 as evaluated across all four different data sequences. Moreover, a comprehensive statistical analysis, using Diebold Mariano Test and boxplots, confirms the further superiority of Stacked-LSTM model to efficiently address inherent uncertainty of solar power generation.
Energy industries. Energy policy. Fuel trade, Renewable energy sources
Energy crisis due to fast globalization and the negative effects of global warming compels a greater need for nonconventional energy sources. Solar and wind power are the primary sources of renewable energy used to meet the power demand. Savonius rotor, a kind of vertical-axis wind turbine, whose operation is dependent upon drag force, has many benefits, including low operating speeds, good self-starting ability, ease of installation, design simplicity, and wind direction independence. However, the low efficiency of the turbine caused by the negative torque it produces on the returning blade limits the applicability of the turbine. The present work experimentally investigated the possibility of performance improvement of the turbine by incorporating inner blades. A 3-bladed configuration of the turbine containing 2 inner blades and an outer semi-circular blade was designed and constructed for performance assessment using an open circuit wind tunnel. Three turbine configurations with 160º inner blade angle and 2 cm spacing between the blades were tested for optimal performance. The performance was evaluated for different wind velocities by fixing the test setup at the wind tunnel outlet. The rotational speed and torque were measured using a rope break dynamometer and tachometer, respectively. The result indicated that using 1 inner arc blade improved the maximum power coefficient and torque coefficient of the rotor by 33.17% and 8.89%, respectively, compared to the conventional configuration. However, using 2 inner blades reduced the maximum power coefficient and torque coefficient by 37.43% and 46.67%, respectively. The maximum value of the power coefficient was found at tip speed ratios (TSRs) between 0.25 and 0.45, whereas the torque coefficient declined with the increase of TSR for all 3 configurations. Moreover, the maximum power coefficient and torque were observed at lower wind speeds.
Energy industries. Energy policy. Fuel trade, Renewable energy sources
Laura Altstadt, Aileen Reichmann, Nora Weber
et al.
Abstract Background Germany’s commitment to climate neutrality by 2045 poses significant challenges for its energy-intensive industries, especially in North Rhine-Westphalia, where green hydrogen is essential for the decarbonisation of basic industries. In this study, we investigate social acceptance of the hydrogen-driven industrial transition, focusing on public perspectives and the perspectives of stakeholders in industry, non-governmental organisations, trade unions, and political administration. Results The results indicate broad support for industrial green hydrogen use but also highlight acceptance issues along its value chain. Key challenges emerge in political, economic, social, and environmental dimensions, with notable public risk perception of hydrogen transport. Our analysis shows that increasing concerns tend to be accompanied by a willingness to protest, while knowledge is associated with acceptance of industrial hydrogen use. Conclusions Stakeholders must find ways to gather and address local public concerns. Moreover, the results indicate the need to assess green hydrogen along its entire value chain and on an international scale.
Renewable energy sources, Energy industries. Energy policy. Fuel trade
Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas
et al.
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.
Abstract Poly(ether ether ketone) (PEEK) is a relevant thermoplastic in industry and in the biomedical sector. In this work, the lubricant capability of graphene nanoplatelets (GNPs) is used for improving the PEEK wear properties. Nanocomposites were prepared by solvent-free melt-blending and injection molding at various compositions between 1 and 10 wt. % of GNPs. The Raman G band shows a progressive increment proportional to the bulk GNP percentage. From calorimetric data, the polymer matrix structure is interpreted in terms of a 3-phase model, in which the crystalline phase fluctuates from 39 to 34% upon GNP addition. Thermal conductivity varies in accordance with the polymer crystallinity. Tensile and flexural tests show a progressive increase in the modulus, as well as a decrease in the fracture strength and the work of fracture. Most important, the composite surface undergoes a substantial improvement in hardness (60%), together with a decrease in the coefficient of friction (−38%) and a great reduction in the wear factor (−83%). Abrasion and fatigue wear mechanisms are predominant at the lowest and highest GNP concentrations respectively. In conclusion, GNPs are used without any chemical functionalization as the filler in PEEK-based materials, improving the surface hardness and the tribological properties.
Stephanie M. Juice, Melannie D. Hartman, Adam C. vonHaden
et al.
ABSTRACT Plant strategies to acquire nutrients from limited environments help shape ecosystem carbon (C) and nitrogen (N) cycling and response to environmental change. The effects of plant strategies on ecosystem dynamics are largely uncharacterized in bioenergy agroecosystems, where the impacts could determine bioenergy's ability to meet its sustainability goals of storing C and reducing N loss. We used FUN‐BioCROP (Fixation and Uptake of Nitrogen‐Bioenergy Carbon, Rhizosphere, Organisms and Protection), a plant–microbe interaction model of coupled plant nutrient uptake and soil organic matter decomposition, to simulate the effects of nutrient acquisition strategies on soil microbial activity and ecosystem nutrient cycling in bioenergy feedstocks miscanthus (Miscanthus × giganteus) and sorghum (Sorghum bicolor (L.) Moench). We examined the model's ability to reproduce the relative effects of belowground nutrient uptake on microbial activity using a reanalysis of empirical data showing that miscanthus root exudation provoked a larger soil microbial response than sorghum. From baseline model simulations, we found that the ability of miscanthus to retranslocate N resulted in higher N uptake at a lower C cost than the sorghum/soybean rotation and that soil C and N pools increased under perennial (miscanthus) and decreased under annual (sorghum/soybean) cultivation. The model also predicted that greater root exudation increased soil C accumulation, highlighting the role of roots in forming stable soil C. Overall, the baseline model was unable to reproduce field observations of miscanthus root exudation stimulating microbial activity more than sorghum. To improve the model, we updated the soil microbial parameters in miscanthus to have faster decomposition, a higher C/N ratio, and greater carbon use efficiency. These changes improved the simulated soil microbial response to miscanthus root exudation, supporting the hypothesis that miscanthus soils foster a microbial community that is more responsive to root exudation than that of sorghum.
Renewable energy sources, Energy industries. Energy policy. Fuel trade
Nebiyu Girgibo, Karita Luokkanen-Rabetino, Pekka Peura
et al.
This article aims to map out a roadmap 2025–2029 for 4 African nations—Ethiopia, Kenya, Uganda, and Botswana—and associated policy recommendations. The method is to work on the project, long-term Joint Relationship Between European and African in Renewable Energy Research in Energy Village Concept in Africa (LEAP-RE: WP 14) and mapping polices and roadmaps from experience and literature. The significance and contribution is an example to African nations for developing and mapping out a Roadmap for 2025–2029 for Energy Village (EV) projects. The novelty is the African Energy Villages, which were identified to be unique and different from implementations in European nations. The EV concept identifies and analyses potential supplies of renewable energy (RE) and local consumption needs to help local communities become energy self-sufficient. Understanding policies and initiatives for self-sufficient RE villages in Africa under the LEAP-RE program is a crucial prerequisite in implementing EV concepts using clean and secured sources of RE such as biomass, small hydropower, solar, and wind for rural African people. The main conclusion is that such EVs are able to use more than 100% RE from local communities to overcome the energy shortage.
Energy industries. Energy policy. Fuel trade, Renewable energy sources
В роботі розглянуто питання вдосконалення роботизованої платформи Niryo One шляхом оптимізації режимів роботи приводу маніпуля-тора. Актуальність дослідження зумовлена необхідністю підвищення ефективності та точності роботи робототехнічних систем у промислових і дослідницьких застосуваннях. У другій частині дослідження представлено розробку математичної моделі динаміки маніпулятора, яка враховує особливості конструкції та параметри платформи Niryo One. На основі побудованої моделі проведено оптимізацію траєкторій руху маніпулятора з використанням методу послідовного квадратичного програмування (SLSQP – Sequential Least Squares Programming). Оптимізація спрямована на міні-мізацію енергоспоживання та часу виконання завдань при дотриманні обмежень на динамічні характеристики системи.
Запропонований підхід до визначення оптимальних режимів руху базується на чисельних методах нелінійного програмування. В результаті оптимізації отримано траєкторії, що забез-печують зниження навантаження на приводи та підвищення плавності руху порівняно з типовими режимами, реалізованими в стандартному програмному забезпеченні платформи. Проведено порівняльний аналіз оптимальних і типових режимів руху за критеріями енергоефективності та динаміки роботи.
Отримані результати можуть бути використані для модернізації існуючих та розробки нових алгоритмів керування двомасовими робото-технічними системами, а також для підвищення надійності та ресурсу роботи. Перспективи подальших досліджень пов’язані з адаптацією розробленого методу для маніпуляторів з іншими кінематичними схемами та в умовах змінних зовнішніх навантажень.
Carlos Roberto Pinheiro Junior, João Luís Nunes Carvalho, Lucas Pecci Canisares
et al.
ABSTRACT Sugarcane straw removal for bioenergy production—especially second‐generation ethanol—is shown to be a promising pathway for decarbonization. However, indiscriminate straw removal can negatively affect soil‐related ecosystem services (SES), compromising the sustainability of the associated bioenergy production. Here, a comprehensive literature review was conducted to select and quantify the changes in agronomic and environmental indicators affected by low (≤ 1/3), moderate (> 1/3 to ≤ 2/3), and high (> 2/3) straw removal levels and the consequential impacts on eight SES. A quali‐quantitative approach was developed to generate an impact matrix that provides the direction of the effects (negative, neutral, or positive) and the associated confidence levels. Overall, the lowest impact on SES occurs under low straw removal with a neutral effect on C storage, nutrient cycling, weed control, greenhouse gas (GHG) mitigation, and provision of food and bioenergy. Water regulation, erosion control, and maintenance of soil biodiversity were the SES most negatively affected by straw removal. Moderate and high levels of straw removal negatively impact the maintenance of SES and compromise the sustainability of sugarcane cultivation areas, except for pest control and soil GHG emission mitigation. Finally, it was also discussed how the negative impacts of straw removal on SES could be mitigated or even reversed through the adoption of best management practices, such as cover crops, organic amendments, biological products (e.g., use of phosphate‐solubilizing bacteria and mycorrhizal fungi), reduced tillage, and machinery traffic control. Ultimately, the results of this study can be useful to guide decision‐making by farmers, investors, stakeholders, and policymakers toward sustainable bioenergy production that contributes to a low‐carbon economy and climate change mitigation.
Renewable energy sources, Energy industries. Energy policy. Fuel trade
Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models to develop more comprehensive and effective AI solutions. Therefore, the effective development and deployment of industrial AI require a more comprehensive and systematic approach. To address this gap, this paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework comprising three core modules: the knowledge module, data module, and model module. These modules help to extend and enhance the industrial AI methodology platform, supporting various industrial applications. In addition, a case study on rotating machinery diagnosis is presented to demonstrate the effectiveness of the proposed framework, and several future directions are highlighted for the development of the industrial AI foundation framework.
Artificial intelligence (AI) has undergone transformative advances since 2022, particularly through generative AI, large language models (LLMs), and diffusion models, fundamentally reshaping the creative industries. However, existing reviews have not comprehensively addressed these recent breakthroughs and their integrated impact across the creative production pipeline. This paper addresses this gap by providing a systematic review of AI technologies that have emerged or matured since our 2022 review, examining their applications across content creation, information analysis, post-production enhancement, compression, and quality assessment. We document how transformers, LLMs, diffusion models, and implicit neural representations have established new capabilities in text-to-image/video generation, real-time 3D reconstruction, and unified multi-task frameworks-shifting AI from support tool to core creative technology. Beyond technological advances, we analyze the trend toward unified AI frameworks that integrate multiple creative tasks, replacing task-specific solutions. We critically examine the evolving role of human-AI collaboration, where human oversight remains essential for creative direction and mitigating AI hallucinations. Finally, we identify emerging challenges including copyright concerns, bias mitigation, computational demands, and the need for robust regulatory frameworks. This review provides researchers and practitioners with a comprehensive understanding of current AI capabilities, limitations, and future trajectories in creative applications.
To address the challenges of 3D modeling and structural simulation in industrial environment, such as the difficulty of equipment deployment, and the difficulty of balancing accuracy and real-time performance, this paper proposes an integrated workflow, which integrates high-fidelity 3D reconstruction based on monocular video, finite element simulation analysis, and mixed reality visual display, aiming to build an interactive digital twin system for industrial inspection, equipment maintenance and other scenes. Firstly, the Neuralangelo algorithm based on deep learning is used to reconstruct the 3D mesh model with rich details from the surround-shot video. Then, the QuadRemesh tool of Rhino is used to optimize the initial triangular mesh and generate a structured mesh suitable for finite element analysis. The optimized mesh is further discretized by HyperMesh, and the material parameter setting and stress simulation are carried out in Abaqus to obtain high-precision stress and deformation results. Finally, combined with Unity and Vuforia engine, the real-time superposition and interactive operation of simulation results in the augmented reality environment are realized, which improves users 'intuitive understanding of structural response. Experiments show that the method has good simulation efficiency and visualization effect while maintaining high geometric accuracy. It provides a practical solution for digital modeling, mechanical analysis and interactive display in complex industrial scenes, and lays a foundation for the deep integration of digital twin and mixed reality technology in industrial applications.
For the current situation of the intelligence, the level of the segment assembly process was lower than it was expected. This paper took the 6-DOFs segment assembly machine designed by Shandong Jiaotong University and Jinan Heavy Industries Group Co., Ltd., as an example and specified the difference between the 6-DOFs segment assembly machine and common 6-DOFs mechanical arms. The core work of the paper contained two aspects. Firstly, the paper analyzed the kinematics mechanism and pointed out the mechanism characteristics of the segment assembly machine which was driven by six hydraulic cylinders and two hydraulic motors. Secondly, for the relationships between the joint rotative angles and the displacement of the driving hydraulic cylinders being more complex and intricate than they were expected, the paper illustrated the reason why there was no analytical solution for this mechanism and provided a simple method to obtain numerical solutions.
Victor Cezar Nepomuceno RIBEIRO, Geraldo BORTOLETTO JÚNIOR
ABSTRACT Brazil stands out as one of the largest manufacturers of MDF (medium density fiberboard) in the world. The industries are concentrated in the south and southeast of the country and are primarily based on the use of Pinus and Eucalyptus wood, which are available in extensive planted areas. In the northern region, there is only one MDF industrial plant. Despite an abundance of potential raw materials in this region, there is a lack of studies on native species wood and their industrial waste utilization for MDF production. The present study aimed to evaluate the properties of MDF manufactured from a mixture of cultivated paricá (Schizolobium amazonicum) wood and wood waste from native Amazonian species. The study assessed the isolated effects of different proportions of the raw materials and panel thicknesses on MDF properties. Panels were produced, and samples were obtained for testing. Using standard procedures, the following properties were determined: density, water absorption, thickness swelling, internal bonding, static bending, and resistance to screw withdrawal. The results revealed a significant impact of the analyzed variables on some physical and mechanical properties of MDF. With the exception of internal bonding, all other properties of the evaluated MDF panels met the specified regulatory requirements for use in furniture manufacturing. It is concluded that mixtures of the assessed raw materials have great potential for MDF production in the furniture industry. However, adjustments in the production process are recommended to improve the internal bonding property.