Search Results (154)

This study presents a detailed experimental comparison of the rotordynamic and thermal performance of automotive turbochargers supported by two distinct hydrodynamic bearing configurations: fully floating ring bearings (FFRBs) and semi-floating ring bearings (SFRBs). While both designs are widely used in commercial turbochargers, they exhibit significantly different dynamic behaviors due to differences in ring motion and fluid film interaction. A cold air-driven test rig was employed to assess vibration and temperature characteristics across a range of controlled lubricant conditions. The test matrix included oil supply pressures from 2 bar (g) to 4 bar (g) and temperatures between 30 °C and 70 °C. Rotor speeds reached up to 200 krpm (thousands of revolutions per minute), and data were collected using a high-speed data acquisition system, triaxial accelerometers, and infrared (IR) thermal imaging. Rotor vibration was characterized through waterfall and Bode plots, while jump speeds and thermal profiles were analyzed to evaluate the onset and severity of instability. The results demonstrate that the FFRB configuration is highly sensitive to oil supply parameters, exhibiting strong subsynchronous instabilities and hysteresis during acceleration–deceleration cycles. In contrast, the SFRB configuration consistently provided superior vibrational stability and reduced sensitivity to lubricant conditions. Changes in lubricant supply conditions induced a jump speed variation in floating ring bearing (FRB) turbochargers that was approximately 3.47 times larger than that experienced by semi-floating ring bearing (SFRB) turbochargers. Furthermore, IR images and oil outlet temperature data confirm that the FFRB system experiences greater heat generation and thermal gradients, consistent with higher energy dissipation through viscous shear. This study provides a comprehensive assessment of both bearing types under realistic high-speed conditions and highlights the advantages of the SFRB configuration in improving turbocharger reliability, thermal performance, and noise suppression. The findings support the application of SFRBs in high-performance automotive systems where mechanical stability and reduced frictional losses are critical. Full article

This study focuses on the multiphase flow in horizontal wells based on distributed acoustic sensing (DAS) monitoring. Through laboratory experiments and field data analysis, it was found that the micro-seismic differences in flow patterns can be clearly observed in the fiber optic micro-seismic waterfall chart. In the case of slug flow, the DAS acoustic energy decreases when the inclination angle increases. The performance of annular flow is similar to that of bubble flow, with the DAS energy increasing as the inclination angle increases. Overall, the order of DAS acoustic energy from the strongest to weakest is slug flow, followed by annular flow, and then bubble flow. The research shows that fiber optic DAS monitoring signals can effectively identify differences in gas volume, well inclination, and flow pattern, which provides an important technical basis and research foundation for the monitoring and analysis of multiphase flow in horizontal wells. Full article

Decision management is the systems engineering life cycle process for making program/system decisions. The purpose of the decision management process is: “…to provide a structured, analytical framework for objectively identifying, characterizing and evaluating a set of alternatives for a decision at any point in the life cycle and select the most beneficial course of action”. Systems engineers and systems analysts need to inform decisions in a digital engineering environment. This paper describes a Decision Analysis Data Model (DADM) developed in model-based systems engineering software to provide the process, methods, models, and data to support decision management. DADM can support digital engineering for waterfall, spiral, and agile development processes. This paper describes the decision management processes and provides the definition of the data elements. DADM is based on ISO/IEC/IEEE 15288, the INCOSE SE Handbook, the SE Body of Knowledge, the Data Management Body of Knowledge, systems engineering textbooks, and journal articles. The DADM was developed to establish a decision management process and data definitions that organizations and programs can tailor for their system life cycles and processes. The DADM can also be used to assess organizational processes and decision quality. Full article

This paper aims to explore the challenges of maintaining and modernizing legacy systems, particularly COBOL-based platforms, the backbone of many financial and administrative systems. By exploring the DOGE team’s initiative to modernize government IT systems on a relevant case study, the author analyzes the pros and cons of AI and Agile methodologies in addressing the limitations of static and highly resilient legacy architectures. A systematic literature review was conducted to assess the state of the art about legacy system modernization, AI integration, and Agile methodologies. Then, the gray literature was analyzed to provide practical insights into how government agencies can modernize their IT infrastructures while addressing the growing shortage of COBOL experts. Findings suggest that AI may support interoperability, automation, and knowledge abstraction, but also introduce new risks related to cybersecurity, workforce disruption, and knowledge retention. Furthermore, the transition from Waterfall to Agile approaches poses significant epistemological and operational challenges. The results highlight the importance of adopting a hybrid human–AI model and structured governance strategies to ensure sustainable and secure system evolution. This study offers valuable insights for organizations that are facing the challenge of balancing the desire for modernization with the need to ensure their systems remain functional and manage tacit knowledge transfer. Full article

Background: Physical and mental health issues are increasingly becoming a global focus of attention, and telemedicine is widely attracting academic interest. Objectives: This exploratory study aimed to investigate the therapeutic potential of immersive virtual blue spaces for individuals with distinct lifestyle backgrounds—specifically, office workers and retirees. The research explores how different virtual waterscapes influence emotional and physiological states in populations with varying stress profiles and life rhythms. Methods: A mixed-methods design was employed, combining quantitative measurements with qualitative interviews. In September 2023, forty participants (20 office workers and 20 retirees) from Hualien, Taiwan, were exposed to 360° VR simulations of three blue environments: a forest stream, a forest waterfall, and a beach scene. Pre- and post-session assessments included physiological indicators (blood pressure and heart rate) and emotional states measured using the Profile of Mood States (POMS) scale. Results: Significant physiological relaxation was observed among retirees. Office workers demonstrated greater emotional improvements, with noticeable variation depending on the type of virtual environment. Comparative analysis highlighted the stream landscape’s unique benefit for reducing depression and enhancing positive mood states. Thematic findings from post-session interviews further indicated that emotional responses were moderated by individual background and prior emotional experiences. Conclusions: These findings underscore the short-term therapeutic potential of virtual blue spaces for diverse user groups and reveal the influence of personal context on their effectiveness. The study supports the integration of VR-based nature exposure into personalized digital healthcare interventions and offers a foundation for future development in immersive therapeutic technologies. Full article

Advancements in artificial intelligence, particularly in computer vision, have driven the research and development of visual food analysis systems focused primarily on enhancing people’s well-being. Food analysis can be performed at various levels of granularity, with food segmentation being a major component of numerous real-world applications. Deep learning-based methodologies have demonstrated promising results in food segmentation; however, many of these approaches demand high computational resources, making them impractical for low-performance devices. In this research, a novel, lightweight, deep learning-based method for semantic food segmentation is proposed. To achieve this, the state-of-the-art DeepLabv3+ model was adapted by optimizing the backbone with the lightweight network EfficientNet-B1, replacing the Atrous Spatial Pyramid Pooling (ASPP) in the neck with Cascade Waterfall ASPP (CWASPP), and refining the encoder output using the squeeze-and-excitation attention mechanism. To validate the method, four publicly available food datasets were selected. Additionally, a new food segmentation dataset consisting of self-acquired food images was introduced and included in the validation. The results demonstrate that high performance can be achieved at a significantly lower cost. The proposed method yields results that are either better than or comparable to those of state-of-the-art techniques while requiring significantly lower computational costs. In conclusion, this research demonstrates the potential of deep learning to perform food image segmentation on low-performance stand-alone devices, paving the way for more efficient, cost-effective, and scalable food analysis applications. Full article

Mushrooms play an important role in ecosystem sustainability and are highly valued in medicine and human nutrition. Using AAS and biochemical methods of analysis, the antioxidant status and mineral composition of seven mushroom species (Armillaria mellea, Xeromocus illudens, Leccinum aurantiacum, Leccinum scrabum, Lactarium pubescens, Rusula vesca, and Lycoperpon molle Pers.) gathered near the Pechenganikel smelting plant in the Pasvik Nature Reserve of the Murmansk region were evaluated. The concentrations of Ni and Cu in the fruiting bodies of mushrooms were in the ranges of 0.43–39.7 and 7.9–45.9 mg kg⁻¹ d.w., respectively. An unusually high biological concentration factor (BCF) for Ni, Cu, and Zn levels in mushrooms grown in soils with a low amount of these elements indicates the low suitability of the mentioned parameter for mushroom characteristics in territories with an uneven distribution of elements in soil. On the other hand, selenium (Se) showed high BCF levels, exceeding 1, for all mushrooms tested, with the highest values associated with L. saccatum (5.17) and the lowest values with A. mellea (1.36). A significant excess (3.4) of the Recommended Daily Allowance (RDA) level per 30 g of dry mushrooms was recorded for Ni in Russula vesca gathered 6 km from the Ni/Cu smelting plant, and 1.3 excess of the RDA was recorded in L. scrabum grown in the vicinity of the Shuonyoka waterfall. No RDA excess was revealed for Cu. Positive correlations between Se, polyphenol content, and total antioxidant activity (AOA) (r = 0.915–0.926; p < 0.001) and a negative correlation between Cu–Se and Cu–AOA in Leccinum species indicate the important role of antioxidant defense and Se, particularly in Arctic mushroom growth and survival, providing a specific protection of mushrooms against Cu toxicity. Full article

Nowadays, reducing energy consumption and obtaining thermal comfort are significant for making educational buildings more climate resilient, more sustainable, and more comfortable. To achieve these goals, a sustainable passive method is that of applying green walls and roofs that provide extra thermal insulation, evaporative cooling, a shadowing effect, and the blockage of wind on buildings. Therefore, the objective of this study is to evaluate the impact of green wall and roof applications on energy consumption and thermal comfort in an educational building. For this purpose, a university building in the Csb climate zone is selected and monitored during one year, as a case study. Then, the case building is modelled in a well-calibrated dynamic building energy simulation tool and twenty-one different plant species, which are mostly used for green walls and roofs, are applied to the envelope of the building in order to determine a reduction in energy consumption and an increase in thermal comfort. The Hedera canariensis gomera (an ivy species) plant is used for green walls due to its aesthetic appeal, versatility, and functional benefits while twenty-one different plants including Ophiopogon japonicus (Mando-Grass), Phyllanthus bourgeoisii (Waterfall Plant), and Phoenix roebelenii (Phoenix Palm) are simulated for the green roof applications. The results show that deploying Hedera canariensis gomera to the walls and Phyllanthus bourgeoisii to the roof could simultaneously reduce the energy consumption by 9.31% and increase thermal comfort by 23.55% in the case building. The authors acknowledge that this study is solely based on simulations due to the high cost of all scenarios, and there are inherent differences between simulated and real-world conditions. Therefore, the future work will be analysing scenarios in real life. Considering the limited studies on the effect of different plant species on energy performance and comfort, this study also contributes to sustainable building design strategies. Full article

Increases in flood magnitude due to climate change increase the necessity of resilient river levees to prevent the breaching that can contribute to reduced flood inundation volume even when overtopping from a levee occurs. When a levee is composed of cohesive soil and the levee crest is paved, overtopping can lead to a waterfall-like nappe flow due to the erosion of the downstream slope of a levee. This flow subsequently expands the scour hole and increases the risk of levee failure. Although some models of scour hole expansion due to nappe flow were proposed, flow structures in the scour hole were not adequately taken into account. This study aimed to clarify the flow structure, including formation of vortices in the scour hole, by conducting flow visualization experiments and three-dimensional numerical analyses. After clarifying the flow structure, this study proposed a simplified model to calculate the bottom shear stress in a scour hole on the levee side. The accuracy of the estimated bottom shear stress was verified by comparing the results with a three-dimensional numerical analysis. This proposed method can predict further erosion of a scour hole. Full article

Plasma–liquid reactions represent an emerging green chemical process for nitrogen fixation; however, these processes generally exhibit low selectivity for ammonium (NH₄⁺). This limitation highlights the need to explore simple methods to increase NH₄⁺; selectivity. In this study, a catalyst-free falling film dielectric barrier discharge plasma system was employed for the selective synthesis of NH₄⁺. By manipulating the flow state of the discharge gas, NH₄⁺ selectivity was found to increase by 138.4% in the sealed gas flow state compared to the flowing gas state. Furthermore, an increase in the discharge voltage positively influenced the NH₄⁺ selectivity. This phenomenon can be attributed to higher energy input and longer reaction times, which facilitate the formation of nitrogen molecular ions, a critical intermediate in ammonia synthesis. The reaction products were analyzed by UV spectrophotometry and emission spectroscopy to investigate the underlying mechanisms of ammonia synthesis. This study reveals the highest reaction rate reported to date for ammonia synthesis via single-system plasma gas–liquid reactions and offers a novel way to improve both the yield and selectivity of ammonium synthesis via non-thermal plasma gas–liquid interactions. Full article

In order to address the challenge posed by existing anti-jamming methods (including intelligent anti-jamming techniques) that struggle to counter high-speed reactive jamming in complex jamming environments, we have developed a novel approach that involves leveraging intelligent jamming in such environments rather than merely attempting to evade jamming. Unlike existing anti-jamming techniques that extract energy from jamming signals as a power source, our proposed method can use intelligent reactive jamming signals as a positive factor in frequency detection. To be precise, we have designed an intelligent multi-sequence frequency hopping communication framework (IMSFH), which includes two stages: communication and training. Firstly, during the synchronous sequence transmission, supervised learning is used in the training stage to obtain the rules of reactive jamming through neural networks. In the communication stage, IMSFH using narrowband reception utilizes reactive jamming rules to improve the frequency-detection capability during actual payload transmission. The simulation results show that this method not only improves communication performance with the increase in jamming signal power and stronger anti-jamming ability when combating high-speed reactive jamming, but also better utilizes reactive jamming to improve communication performance in complex jamming environments. Full article

Due to the large number of parameters and high computational complexity of current target detection models, it is challenging to perform fast and accurate target detection in side-scan sonar images under the existing technical conditions, especially in environments with limited computational resources. Moreover, since the original waterfall map of side-scan sonar only consists of echo intensity information, which is usually of a large size, it is difficult to fuse it with other multi-source information, which limits the detection accuracy of models. To address these issues, we designed DBnet, a lightweight target detector featuring two lightweight backbone networks (PP-LCNet and GhostNet) and a streamlined neck structure for feature extraction and fusion. To solve the problem of unbalanced aspect ratios in sonar data waterfall maps, DBnet employs the SAHI algorithm with sliding-window slicing inference to improve small-target detection accuracy. Compared with the baseline model, DBnet has 33% fewer parameters and 31% fewer GFLOPs while maintaining accuracy. Tests performed on two datasets (SSUTD and SCTD) showed that the mAP values improved by 2.3% and 6.6%. Full article

Background/Objectives: Antimicrobial resistance is a global threat to safe health care, and a reduction in antibiotic consumption seems to be an appropriate preventive measure. In Germany, the reporting of hospital antibiotics consumption to an independent institution is only voluntary. Although a high level of willingness to improve can be assumed in the case of participation, the median consumptions of reporting hospitals change only slightly. This study examines the question of whether the logistical consumption figures adequately reflect real consumption, and if not, how to optimize the use of logistical data for clinical decisions. Methods: Four selected wards were analyzed during six months. A retrospective analysis of patient case files was performed to receive “prescribed daily doses” (PDDs). These were compared to “defined daily doses” (DDDs) from logistical data. Additional inventories were performed to calculated stored antibiotics. Antibiotics goods flows were presented via waterfall diagrams to identify logistic patterns that could explain PDD/DDD quotients. Antimicrobial stewardship (AMS) quality indicators were analyzed to give advice for optimized clinical AMS measures. Results: The total PDD/DDD quotient was 0.69. Four logistical patterns were identified. Optimized prophylaxis, AMS consultations and reevaluation of therapy seem to be the most useful measures to reduce PDDs. Conclusions: If AMS programs rely solely on DDDs, measures cannot be optimal. A complete consideration of antibiotic goods flows supports clinical decisions, but is very costly in terms of data collection. The consideration of logistical data can help to identify areas of focus for AMS programs. Therefore, specialists of antibiotics logistics should complement clinical AMS teams. Full article

Carnot treats Heat as a Force of Nature, with its typical fundamental characteristics of intensity and thermal tension (temperature and temperature difference), extension (amount of heat, i.e., caloric), and power. To suggest how the three aspects are related, he applies the imagery of waterfalls to causative thermal processes: heat powers motion in a heat engine just as falling water does when activating rotation in a water wheel. We understand Carnot’s waterfall imagery as an archetype of human reasoning—as an embodiment of how we experience and understand causative (agentive) phenomena. We project it onto the macroscopic phenomena identified in physical science and so unlock the power of analogical structure mapping between theories of fluids, electricity and magnetism, heat, substances, gravity, and linear and rotational motion. In particular, the notion of (motive) power of a waterfall lets us create imaginative explanations of the interactions of Forces of Nature and helps us construct a generalized energy principle. Two-hundred years after Carnot made us aware of it, his Waterfall Analogy is a powerful example of theory construction with roots deep in how we experience phenomena as caused by natural agents. Full article

Entropy and energy had not yet been introduced to physics by the time Carnot wrote his seminal Réflexions. Scholars continue to discuss what he really had in mind and what misconceptions he might have had. Actually, his work can be read as a correct introduction to the physics of heat engines when the term calorique is replaced by entropy and entropy is used as the other fundamental thermal quantity besides temperature. Carnot’s concepts of falling entropy as an analogy to the waterfall, and the separation of real thermal processes into reversible and irreversible processes are adopted. Some details of Carnot’s treatise are ignored, but the principal ideas are quoted and assumed without modification. With only two thermal quantities, temperature and entropy, modern heat engines can be explained in detail. Only after the principal function of heat engines is developed is energy introduced as physical quantity in order to compare thermal engines with mechanical and electrical engines and, specifically, to calculate efficiency. Full article