Search Results (115,659)

Hydrocarbon fluids have a widespread presence in modern society due to their role in the global energy and fuel supply. The ability to distinguish between hydrocarbon fluids from different manufacturing processes is essential in industrial and government settings. Currently, performing such analyses is expensive and time-consuming, as standard practice involves sending samples to a laboratory for gas chromatography-mass spectrometry (GC-MS) analysis. The inherent limitations of traditional separation techniques often make them unsuitable for the demands of real-time process monitoring and control. This work proposes the use of handheld Raman spectroscopy for rapid classification of petroleum- and synthetic-based hydrocarbon fluids. A total of 600 Raman spectra were collected from six different hydraulic fluids and analyzed. Preliminary visual observations revealed reproducible spectral differences between various types of hydraulic fluids. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate the data further. The findings indicate that handheld Raman spectrometers are capable of detecting chemical features of hydrocarbon fluids, supporting the classification of their formulations. Full article

A very important method of reducing carbon emissions is to make sure industrial plants are operated at optimal energy efficiency. The oil and petrochemical industries spend large amounts of energy in the transportation of petroleum and its various products that have high viscosities. A critical component in these plants is abrupt pipe contraction. Large amounts of energy are lost in pipe contractions. In this paper we investigate the energy losses in pipe contraction using the local entropy generation method after solving the detailed flow field around an abrupt pipe contraction. We have applied the method at various Reynolds numbers covering laminar and turbulent flow regimes. Furthermore, we have used an integral entropy analysis and found excellent agreement between the differential and integral entropy methods when the computational grid is well refined. The differential analysis was able to predict the local entropy generation and find where the large losses are located and therefore be able to minimize these losses effectively. Based on the detailed entropy generation field, it is recommended to use rounded contraction in order to reduce the losses. By introducing rounded contractions in laminar flow, the losses have been reduced by 22%. In the case of the turbulent flow regime, the losses were reduced by 96% by introducing a rounding radius to diameter ratio r/D₂ of 10%. The turbulent flow results for the case of pipe entrance, which is a special case of abrupt contraction (D₂/D₁ goes to zero) agree very well with the present results. This work addresses a large range of D₂/D₁ for laminar and turbulent flows. It is recommended that companies involved in designing petrochemical plants and installations take these findings into consideration to reduce carbon emissions. These recommendations also extend to the design of equipment and piping systems for the food industry and micro-device flows. Full article

As academic institutions expand, the proliferation of laboratories dealing with hazardous chemicals has risen. While the physicochemical characterization equipment employed in these academic chemical laboratories is widely recognized, its usage presents a notable risk to researchers at various levels. This paper presents a simplified approach for evaluating the effects of the implementation of prevention investments in regard to working with nanomaterials on a lab scale. The evaluation is based on modeling the benefits (avoided accident costs) and costs (safety training), as opposed to an alternative (not investing in safety training). Each scenario analyzed in the economic evaluation reflects a different level of risk. The novelty of this study lies in its objective to provide an economic assessment of the benefits and returns from safety investments—specifically training—in a chemical laboratory, using a framework that integrates qualitative insights to explore and define the context alongside quantitative data derived from a cost–benefit analysis. The Net Present Value (NPV) was evaluated. The results of the cost–benefit analysis demonstrated that the benefits exceed the cost of the investment. The findings from the sensitivity analysis highlight the significant influence of insurance benefits on safety investments in the specific case study. In this case study, the deterministic analysis yielded a Net Present Value (NPV) of €280,414.67, which aligns closely with the probabilistic results. The probabilistic NPV indicates 90% confidence that the investment will yield a positive NPV ranging from €283,053 to €337,356. The cost–benefit analysis results demonstrate that the benefits outweigh the costs, showing that with an 87% training success rate, this investment would generate benefits of approximately €6328 by preventing accidents in this study. To the best of the researchers’ knowledge, this is the first study to evaluate the influence of safety investment through an economic evaluation of laboratory accidents with small-angle X-ray scattering during the physicochemical characterization process of engineered nanomaterials. The proposed approach and framework are relevant not only to academic settings but also to industry. Full article

With industrial transformation and the rise in the 15 min community life circle, optimizing walkability and preserving industrial heritage are key to revitalizing former industrial areas. This study, focusing on Shijingshan District in Beijing, proposes a walkability evaluation framework integrating multi-source big data and street-level perception. Using Points of Interest (POI) classification, which refers to the categorization of key urban amenities, pedestrian network modeling, and street view image data, a Walkability Friendliness Index is developed across four dimensions: accessibility, convenience, diversity, and safety. POI data provide insights into the spatial distribution of essential services, while pedestrian network data, derived from OpenStreetMap, model the walkable road network. Street view image data, processed through semantic segmentation, are used to assess the quality and safety of pedestrian pathways. Results indicate that core communities exhibit higher Walkability Friendliness Index scores due to better connectivity and land use diversity, while older and newly developed areas face challenges such as street discontinuity and service gaps. Accordingly, targeted optimization strategies are proposed: enhancing accessibility by repairing fragmented alleys and improving network connectivity; promoting functional diversity through infill commercial and service facilities; upgrading lighting, greenery, and barrier-free infrastructure to ensure safety; and delineating priority zones and balanced enhancement zones for differentiated improvement. This study presents a replicable technical framework encompassing data acquisition, model evaluation, and strategy development for enhancing walkability, providing valuable insights for the revitalization of industrial districts worldwide. Future research will incorporate virtual reality and subjective user feedback to further enhance the adaptability of the model to dynamic spatiotemporal changes. Full article

Brewer’s spent grain (BSG), a by-product originating from the brewing industry, contains substantial amounts of fibers, proteins, and bioactive compounds; however, its utility is restricted by anti-nutritional factors. Solid-state fermentation (SSF) presents a viable method for improving the nutritional and functional properties of BSG. Microorganisms such as Rhizopus oligosporus have been demonstrated to enhance nutrient bioavailability, facilitate the degradation of complex carbohydrates, and improve protein digestibility while simultaneously reducing anti-nutritional components. Furthermore, this fermentation process yields bioactive compounds that exhibit antioxidant, anti-inflammatory, and prebiotic properties, thereby contributing to improved gut health, the prevention of metabolic disorders, and enhanced nutritional outcomes. Additionally, SSF seeks sustainability by repurposing agro-industrial by-products, reducing waste, and promoting the principles of a circular economy. Collectively, these advantages underscore the transformative potential of SSF in converting BSG into a functional food ingredient, effectively addressing contemporary health and environmental challenges and offering innovative solutions for food security and sustainable development. Full article

Multirotor UAVs powered by batteries face limitations due to the low energy density of their energy source, which constitutes a significant portion of the total weight. During missions, the high battery mass remains constant, necessitating high required power. This leads to reductions in payload capacity and endurance constraints. This study developed a design tool for multirotor UAVs that sequentially detach used batteries during missions to reduce weight and extend endurance. The developed tool consists of a battery weight prediction model and a required power prediction model. It accurately predicts endurance by considering changes in weight, thrust, RPM, motor-propeller efficiency, and required power at each battery separation point. Using the developed tool, the battery separation technology was applied to a quadcopter with total weights of 7, 15, and 25 kg, and the extended endurances were quantitatively compared. The results showed endurance improvements of 127.3%, 122.0%, and 127.0% for the 7, 15, and 25 kg quadcopters, respectively, compared to using a single battery. In addition, the method was applied to the commercially available industrial UAV DJI Matrice 300 RTK. With a 2.7 kg payload, the two-stage battery configuration extended the endurance by 12.5% compared to the single-battery case. Under no-payload conditions, a three-stage configuration achieved a 16.7% improvement. These results confirm the effectiveness of staged battery detachment even in real-world UAV platforms. Full article

Bearings are vital to rotating machinery, where undetected faults can cause severe failures. Conventional fault diagnosis methods depend on manual feature engineering and labeled data, struggling with complex industrial conditions. This study introduces an innovative unsupervised framework combining masked self-supervised learning with the Swin Transformer for bearing fault diagnosis. The novel integration leverages masked Auto Encoders to learn robust features from unlabeled vibration signals through reconstruction-based pretraining, while the Swin Transformer’s shifted window attention mechanism enhances efficient capture of fault-related patterns in long-sequence signals. This approach eliminates reliance on labeled data, enabling precise detection of unknown faults. The proposed method achieves 99.53% accuracy on the Paderborn dataset and 100% accuracy on the CWRU dataset significantly, surpassing other unsupervised Auto Encoder-based methods. This method’s innovative design offers high adaptability and substantial potential for predictive maintenance in industrial applications. Full article

By integrating cathodic arc evaporation (CAE) with magnetron sputtering (MS) or high-power impulse magnetron sputtering (HiPIMS), hard coatings with diverse multicomponent compositions can be fabricated. Depending on the deposition conditions, the coatings with nano-composite or nano-multilayered microstructures are produced. During the mixing deposition conditions, nano-composite coatings are fabricated, which can be tailored to possess combining properties of super hardness, low friction coefficient, and excellent thermal/chemical stability. For the deposition with larger rotating periods, layer-by-layer deposition was observed. By the nano-multilayered coating design, superior mechanical properties (hardness ≥ 35 GPa), modulated residual stresses, and enhanced high-temperature properties can be obtained. In addition, lubricious elements, low friction (friction coefficient < 0.4), and low wear (<10⁻⁵ mm³/N∙m) both at ambient temperature and high temperature can be realized. Among these coatings, some have been specifically designed to achieve outstanding cutting performance in high-speed cutting applications. Several nitride and oxide hard coatings, such as AlTiN, TiAlN/TiSiN, AlCrN/Cu, and AlCrO, were deposited using a hybrid industrial physical vapor deposition (PVD) coating system. The microstructure, mechanical properties, and cutting performance of these coatings will be discussed. Full article

Four biomass residues–rosemary pomace, rosemary cake, grape seed and apple pomace–were torrefied at 250, 350 and 450 °C, and the physical, chemical and structural changes were characterized. The mass and energy yield decreased with increasing torrefaction temperature; the lowest mass (~10.4%) and energy yield (~10.6%) were observed for rosemary cake torrefied at 450 °C. The HHV increased the most for all feedstocks at 350 °C, with rosemary cake reaching a peak value of 36.4 MJ/kg at 350 °C. Ash content increased with temperature due to organic mass loss, while volatiles decreased and fixed carbon increased in most samples. The FTIR spectra showed the progressive loss of hydroxyl, carbonyl and C–O functionalities and the appearance of aromatic C=C bonds, indicating the formation of the biochar. TGA and DTG analyses revealed that the torrefied samples exhibited higher initial and maximum temperatures for decomposition, confirming improved thermal stability. The TGA-FTIR analyses of gas emissions during pyrolysis and combustion showed that the emissions of CO₂, CH₄, NO_x and SO₂ decreased with increasing degree of torrefaction. Overall, 350 °C was optimal to maximize energy density. The results show that agro-industrial residues can be effectively converted into sustainable biofuels, which offer the dual benefit of reducing waste disposal problems and providing a renewable alternative. In practice, such residues could be used for decentralized power generation in rural areas, co-combustion in existing power plants, or as feedstock for advanced bioenergy systems. Full article

Against the dual backdrop of iterative AI advancement and deepening green development imperatives, AI-driven industrial intelligence (INT) has emerged as a pivotal force in driving sustainable economic growth. While the existing literature has explored the correlation between INT and green total factor productivity (GTFP), significant gaps remain in the design of multidimensional variables, analysis of environmental regulation (ER), and capture of dynamic effects. From the perspective of ER, this study utilizes provincial panel data from China (2012–2023) to construct an 11-indicator evaluation system for INT development and employs the EBM super-efficiency model to measure GTFP. Furthermore, a two-way fixed effects model combined with a moderated mediation model is established to systematically elucidate the intrinsic linkage mechanism between INT and GTFP. The key findings are as follows: First, INT has a significant positive impact on GTFP. Second, green innovation and spatio-economic synergy are crucial pathways through which INT empowers GTFP. Third, ER exhibits a substitutive effect within both the direct and indirect impacts of INT on GTFP, where intensified ER significantly attenuates INT’s positive impacts. Fourth, the enhancement effect of INT on GTFP remains statistically significant with a one-year lag, and the substitution effect of ER persists. This study provides an in-depth analysis of the mechanisms of INT-driven green economic transformation, offering valuable insights for governments to implement differentiated environmental governance strategies tailored to local conditions. Full article

The purpose of this study is to systematically examine the impact of technological innovation on the coordinated development of regional economies and its internal mechanism. It is aimed at revealing whether and how technological innovation promotes the coordinated development of regional economies, and further identifying its heterogeneity characteristics and boundary conditions in the space–time dimension. The research was conducted using panel data for 258 prefecture-level cities in China from 2011 to 2021. This study found that technological innovation significantly promoted the coordinated development of regional economies; this effect was more prominent in China’s eastern region and the Yangtze River Economic Belt. The mechanism test shows that technological innovation can optimize regional resource allocation and narrow the development gap by promoting industrial structure upgrades and rationalization. Further analysis shows that the level of marketization has a nonlinear regulatory effect on the coordination effect of technological innovation, with two threshold levels. A heterogeneity analysis reveals significant differences in the effects of technological innovation in different regions in China, especially in the western region and the northwest side of the Hu Changyong line. The research leads to four key policy recommendations. First, it is important to strengthen the core driving role of technological innovation and implement regionally differentiated innovation support policies. Second, industrial structure upgrades should be encouraged through industrial chain coordination. The third recommendation is to improve the market-oriented institutional environment and minimize barriers to factor flow. Fourth, supporting coordinated policies, such as optimizing human capital and introducing high-quality foreign capital, is necessary to establish a sustainable long-term mechanism for regional coordinated development. Full article

As a key connection technology in prefabricated buildings, offshore wind power, and bridge engineering, the performance and environmental sustainability of grouted sleeve connections are essential for the long-term development of civil infrastructure. To address the environmental burden of conventional high-strength cement-based grouts, an eco-friendly sleeve grouting material incorporating industrial solid waste was developed. In this study, silica fume (15%) and fly ash (5%) were employed as supplementary cementitious materials, while nanosilica (NS) was introduced to enhance the material properties. Mechanical testing, microstructural characterization, and half-grouted sleeve uniaxial tensile tests were conducted to systematically evaluate the effect of NS content on grout performance. Results indicate that the incorporation of NS significantly accelerates the hydration of silica fume and fly ash. At an optimal dosage of 0.4%, the 28-day compressive strength reached 105.5 MPa, representing a 37.9% increase compared with the control group without NS. In sleeve tensile tests, specimens with NS exhibited reinforcement necking failure, and the load–displacement response closely aligned with the stress–strain behavior of the reinforcement. A linear relationship was observed between sleeve wall strain and reinforcement stress, confirming the cooperative load-bearing behavior between the grout and the sleeve. These findings provide theoretical guidance and technical support for developing high-strength, low-impact grouting materials suitable for sustainable engineering applications. Full article

Time series clustering plays a vital role in various analytical and pattern recognition tasks by partitioning structurally similar sequences into semantically coherent groups, thereby facilitating downstream analysis. However, building high-quality clustering models remains challenging due to three key issues: (i) capturing dynamic shape variations across sequences, (ii) ensuring discriminative cluster structures, and (iii) enabling end-to-end optimization. To address these challenges, we propose KDiscShapeNet, a structure-aware clustering framework that systematically extends the classical k-Shape model. First, to enhance temporal structure modeling, we adopt Kolmogorov–Arnold Networks (KAN) as the encoder, which leverages high-order functional representations to effectively capture elastic distortions and multi-scale shape features of time series. Second, to improve intra-cluster compactness and inter-cluster separability, we incorporate a dual-loss constraint by combining Center Loss and Supervised Contrastive Loss, thus enhancing the discriminative structure of the embedding space. Third, to overcome the non-differentiability of traditional K-Shape clustering, we introduce Differentiable k-Shape, embedding the normalized cross-correlation (NCC) metric into a differentiable framework that enables joint training of the encoder and the clustering module. We evaluate KDiscShapeNet on nine benchmark datasets from the UCR Archive and the ETT suite, spanning healthcare, industrial monitoring, energy forecasting, and astronomy. On the Trace dataset, it achieves an ARI of 0.916, NMI of 0.927, and Silhouette score of 0.931; on the large-scale ETTh1 dataset, it improves ARI by 5.8% and NMI by 17.4% over the best baseline. Statistical tests confirm the significance of these improvements (p < 0.01). Overall, the results highlight the robustness and practical utility of KDiscShapeNet, offering a novel and interpretable framework for time series clustering. Full article

In this study, the effects of plasma assistance on the electron beam physical vapour deposition (EB-PVD) process were investigated using an industrial coater (Smart Coater ALD Vacuum Technologies GmbH) equipped with a dual hollow cathode system. This configuration enabled the generation of a plasma environment during the deposition of the ceramic top coat onto a metallic substrate. The objective was to assess how plasma assistance influences the microstructure and thickness distribution of 7% wt. yttria-stabilised zirconia (YSZ) thermal barrier coatings (TBCs). Coatings were deposited with and without plasma assistance to enable a direct comparison. The thickness uniformity and columnar morphology of the 7YSZ top coats were evaluated by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The mechanical properties of the deposited coatings were verified by the scratch test method. The results demonstrate that, in the presence of plasma, columnar grains become more uniformly spaced and exhibit sharper, well-defined boundaries even at reduced substrate temperatures. XRD analysis confirmed that plasma-assisted EB-PVD processes allow for maintaining the desired tetragonal phase of YSZ without inducing secondary phases or unwanted texture changes. These findings indicate that plasma-assisted EB-PVD can achieve desirable coating characteristics (uniform thickness and optimised columnar structure) more efficiently, offering potential advantages for high-temperature applications in aerospace and power-generation industries. Continued development of the EB-PVD process with the assistance of plasma generation could further improve deposition rates and TBC performance, underscoring the promising future of HC-assisted EB-PVD technology. Full article

The strategic goal of the transition to a low-carbon economy in Russia requires the active integration of forest-climatic projects into regional sustainable development strategies, especially for areas with high agricultural pressure such as the central forest-steppe of the European part of the Russian Federation. The region contains over 18 million hectares of forest land, which is approximately 2.1% of the area of Russian forests, and intensive agricultural development increases the need for innovative approaches to restoring forest ecosystems. The work uses indicators of the state forest register, data on 18 reforestation projects and 22 afforestation projects, and the results of forecasting the dynamics of greenhouse gas absorption until 2030. It is estimated that by 2030, the sequestration potential of the forests of the central forest-steppe can be increased by 28–30%, which will neutralize up to 12% of emissions from industrial enterprises in the region. In the paper, to unify the assessment, it is proposed to use the carbon intensity factor of investment costs, which, in a number of implemented projects, ranged from 1.2 to 2.7 RUB/1 kg CO₂ eq., reflecting the cost of achieving one ton of absorbed CO₂ equivalent. At ratios above 1, the economic value of the carbon units created exceeds investment costs by at least 20%. Environmental–economic modeling showed that with an increase in the forest cover of the region by 1% (180 thousand hectares), the annual absorption of CO₂ increases by approximately 0.9–1.1 million tons, and the increase in potential income from the sale of carbon units could amount to 1.6–2.2 billion RUB per year at the current price of 1.8–2 RUB/kg CO₂-eq. The use of an integral criterion of environmental and economic efficiency helps increase the transparency and investment-attractiveness of forest-climatic projects, as well as the effective integration of natural and climatic solutions into long-term strategies for the sustainable development of the Central Forest-Steppe of Russia. Full article