Search Results (80,753)

The optimization of high-frequency circuits remains a computationally intensive task due to the need for repeated high-fidelity electromagnetic (EM) simulations. To address this challenge, we propose a novel integration of machine learning-generated inverse maps within the space mapping (SM) optimization framework to significantly accelerate circuit optimization while maintaining high accuracy. The proposed approach leverages Bayesian Neural Networks (BNNs) and surrogate modeling techniques to construct an inverse mapping function that directly predicts design parameters from target performance metrics, bypassing iterative forward simulations. The methodology was validated using a low-pass filter optimization scenario, where the inverse surrogate model was trained using electromagnetic simulations from COMSOL Multiphysics 2024 r6.3 and optimized using MATLAB R2024b r24.2 trust region algorithm. Experimental results demonstrate that our approach reduces the number of high-fidelity simulations by over 80% compared to conventional SM techniques while achieving high accuracy with a mean absolute error (MAE) of 0.0262 (0.47%). Additionally, convergence efficiency was significantly improved, with the inverse surrogate model requiring only 31 coarse model simulations, compared to 580 in traditional SM. These findings demonstrate that machine learning-driven inverse surrogate modeling significantly reduces computational overhead, accelerates optimization, and enhances the accuracy of high-frequency circuit design. This approach offers a promising alternative to traditional SM methods, paving the way for more efficient RF and microwave circuit design workflows. Full article

Monitoring the activity index of animals is crucial for assessing their welfare and behavior patterns. However, traditional methods for calculating the activity index, such as pixel intensity differencing of entire frames, are found to suffer from significant interference and noise, leading to inaccurate results. These classical approaches also do not support group or individual tracking in a user-friendly way, and no open-access platform exists for non-technical researchers. This study introduces an open-source web-based platform that allows researchers to calculate the activity index from top-view videos by selecting individual or group animals. It integrates Segment Anything Model2 (SAM2), a promptable deep learning segmentation model, to track animals without additional training or annotation. The platform accurately tracked Cobb 500 male broilers from weeks 1 to 7 with a 100% success rate, IoU of 92.21% ± 0.012, precision of 93.87% ± 0.019, recall of 98.15% ± 0.011, and F1 score of 95.94% ± 0.006, based on 1157 chickens. Statistical analysis showed that tracking 80% of birds in week 1, 60% in week 4, and 40% in week 7 was sufficient (r ≥ 0.90; p ≤ 0.048) to represent the group activity in respective ages. This platform offers a practical, accessible solution for activity tracking, supporting animal behavior analytics with minimal effort. Full article

Cucumbers (Cucumis sativus L.) are highly sensitive to cold, but grafting onto cold-tolerant rootstocks can enhance their low-temperature resilience. This study investigates the physiological and molecular mechanisms by which exogenous vitamin C (Vc) mitigates cold stress in grafted cucumber seedlings. Using cucumber ‘Chiyu 505’ as the scion and pumpkin ‘Chuangfan No.1’ as the rootstock, seedlings were grafted using the whip grafting method. In the third true leaf expansion stage, seedlings were foliar sprayed with Vc at concentrations of 50, 100, 150, and 200 mg L⁻¹. Three days after initial spraying, seedlings were subjected to cold stress (8 °C) for 3 days, with continued spraying. After that, morphological and physiological parameters were assessed. Results showed that 150 mg L⁻¹ Vc treatment was most impactive, significantly reducing the cold damage index while increasing the root-to-shoot ratio, root vitality, chlorophyll content, and activities of antioxidant enzymes (SOD, POD, CAT). Moreover, this treatment enhanced levels of soluble sugars, soluble proteins, and proline compared to control. However, 200 mg L⁻¹ treatment elevated malondialdehyde (MDA) content, indicating potential oxidative stress. For transcriptomic analysis, leaves from the 150 mg L⁻¹ Vc and CK treatments were sampled at 0, 1, 2, and 3 days of cold stress. Differential gene expression revealed that genes associated with photosynthesis (LHCA1), stress signal transduction (MYC2-1, MYC2-2, WRKY22, WRKY2), and antioxidant defense (SOD-1, SOD-2) were initially up-regulated and subsequently down-regulated, as validated by qRT-PCR. Overall, we found that the application of 150 mg L⁻¹ Vc enhanced cold tolerance in grafted cucumber seedlings by modulating gene expression networks related to photosynthesis, stress response, and the antioxidant defense system. This study provides a way for developing Vc biostimulants to enhance cold tolerance in grafted cucumbers, improving sustainable cultivation in low-temperature regions. Full article

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

This paper introduces a novel fractional-order model using the Caputo derivative operator to investigate the virus dynamics of adaptive immune responses. Two infection routes, namely cell-to-cell and virus-to-cell transmissions, are incorporated into the dynamics. Our research establishes the existence and uniqueness of positive and bounded solutions through the application of the generalized mean-value theorem and Banach fixed-point theory methods. The fractional-order model is shown to be Ulam–Hyers stable, ensuring the model’s resilience to small errors. By employing the normalized forward sensitivity method, we identify critical parameters that profoundly influence the transmission dynamics of the fractional-order virus model. Additionally, the framework of optimal control theory is used to explore the characterization of optimal adaptive immune responses, encompassing antibodies and cytotoxic T lymphocytes (CTL). To assess the influence of memory effects, we utilize the generalized forward–backward sweep technique to simulate the fractional-order virus dynamics. This study contributes to the existing body of knowledge by providing insights into how the interaction between virus-to-cell and cell-to-cell dynamics within the host is affected by memory effects in the presence of optimal control, reinforcing the invaluable synergy between fractional calculus and optimal control theory in modeling within-host virus dynamics, and paving the way for potential control strategies rooted in adaptive immunity and fractional-order modeling. Full article

According to the United Nations Environment Programme reports, buildings are responsible for nearly 40% of energy-related emissions; therefore, energy-optimized building design is crucial to reduce the reliance on non-renewable energy sources as well as greenhouse gas emissions. The OECD reports indicate the use of Building Information Modelling (BIM) as one of the effective strategies for decarbonization of buildings, since a 3D digital representation of both physical and functional characteristics of a building can help to design a more efficient infrastructure. An efficient integration of solar energy in building design can be vital for the enhancement of energy performance in terms of heating, cooling, and lighting demand. This paper presents results of an analysis of how factors related to the use of daylight, such as automatic control of artificial lighting, external shading, or the visual absorptance of internal surfaces, influence the energy efficiency within an example room in two different climatic zones. The simulation was conducted using Design Builder software, with predefined occupancy schedules and internal heat gains, and standard EPW weather files for Warsaw and Genua climate zones. The study indicates that for the examined room, when no automatic sunshades or a lighting control system is utilized, most of the final energy demand is for cooling purposes (45–54%), followed by lighting (42–43%), with only 3–12% for heating purposes. The introduction of sunshades and/or the use of daylight allowed for a reduction of the total demand by up to half. Moreover, it was pointed out that often neglected factors, like the colour of the internal surfaces, can have a significant effect on the final energy consumption. In variants with light interior, the total energy consumption was lower by about 3–4% of the baseline demand, compared to their corresponding ones with dark surfaces. These results are consistent with previous studies on daylighting strategies and highlight the importance of considering both visual and thermal impacts when evaluating energy performance. Similarly, possible side effects of certain actions were highlighted, such as an increase in heat demand resulting from a reduced need for artificial lighting. The results of the analysis highlight the potential of a simulation-based design approach in optimizing daylight use, contributing to the broader goals of building decarbonization. Full article

We need to educate students and the public about addressing natural resource challenges to maintain civilization moving into a sustainable future. Because US mineral and energy resources are found in its continental crust and sedimentary basins, introductory geology students need to be well-informed about US crust and basins. We think that creating effective videos about these topics is the best way to engage students to want to learn more. In preparation for making these videos, we researched what introductory geology students are taught and what they learn about these topics. Student interviews informed us about learned curriculum, and taught curriculum was analyzed using a novel keyword-counting method applied to textbook indices. We found that geophysics is stressed twice as much as geology, radiometric dating, and sedimentary basins. We expected that students would have learned more about geophysics and less about the other topics; however, this was not the case. Students knew more about geology, and less about geophysics, radiometric dating, and sedimentary basins. To make effective videos on these topics, we need to explain the following threshold concepts: seismic refraction to scaffold student understanding of crustal geophysics, as well as radiometric dating and deep time to understand crustal geology and the economic importance of sedimentary basins. Full article

The design and planning of Western-style constructions during the early Qing Dynasty in China constituted a significant multicultural encounter that fused technological advancement with aesthetic innovation. This cultural interplay is particularly evident in the imperial garden and pavilion projects commissioned by the Qing court, which served as physical and symbolic sites of cross-cultural dialogue. Influenced by the intellectual and artistic movements of the European Renaissance, Western architectural concepts gradually found their way into the spatial and visual language of Chinese architecture, especially within the royal gardens and aristocratic buildings of the time. These structures were not simply imitative but rather represented a selective adaptation of Western ideas to suit Chinese imperial tastes and principles. This article examines the architectural language that emerged from this encounter between Chinese and European cultures, analysing symbolic motifs, spatial design, ornamental aesthetics, the application of linear perspective, and the integration of foreign architectural forms. These elements collectively functioned as tools to construct a unique visual discourse that communicated both political authority and cultural hybridity. The findings underscore that this architectural phenomenon was not merely stylistic imitation, but rather a dynamic convergence of technological knowledge and artistic vision across cultural boundaries. Full article

This study investigates the use of waste glass powder derived from fluorescent lamps as a partial replacement for cement in mortar production, aiming to valorize this Waste from Electrical and Electronic Equipment (WEEE) and enhance sustainability in the construction sector. Mortars were formulated by substituting 25% of cement by volume with glass powders from fluorescent lamp glass and green bottle glass. The experimental program evaluated mechanical strength, durability parameters and ecotoxicological performance. Results revealed that clean fluorescent lamp mortars showed the most promising mechanical behavior, exceeding the reference in long-term compressive (54.8 MPa) and flexural strength (10.0 MPa). All glass mortars exhibited significantly reduced chloride diffusion coefficients (85–89%) and increased electrical resistivity (almost 4 times higher), indicating improved durability. Leaching tests confirmed that the incorporation of fluorescent lamp waste did not lead to hazardous levels of heavy metals in the cured mortars, suggesting effective encapsulation. By addressing both technical (mechanical and durability) and ecotoxic performance, this research contributes in an original and relevant way to the development of more sustainable building materials. Full article

With the continuous increase in urban population, the subway is the main way to alleviate traffic congestion. However, the construction environment of subway stations is complex, and the safety risks are extremely high. Therefore, it is of great practical significance to scientifically and systematically evaluate the safety vulnerability of subway station construction. This paper takes the Chengdu subway project as an example, and establishes a metro station construction safety vulnerability evaluation index system based on the driving forces–pressures–state–impacts–responses (DPSIR) theory with 5 first-level indexes and 23 second-level indexes, and adopts the fuzzy hierarchical analysis method (FAHP) to calculate the subjective weights, and the improved Harris Hawks optimization–projection pursuit method (HHO-PPM) to determine the objective weights, combined with game theory to calculate the comprehensive weights of the indicators, and finally uses the improved cloud model of Bayesian feedback to determine the vulnerability level of subway station construction safety. The study found that the combined empowerment–improvement cloud model assessment method is reliable, and the case study verifies that the vulnerability level of the project is “very low risk”, and the investigations of safety hazards and the pressure of surrounding traffic are the key influencing factors, allowing for the proposal of more scientific and effective management strategies for the construction of subway stations. Full article

The advancement of Artificial Intelligence (AI) has significantly accelerated progress across various research domains, with growing interest in plant science due to its substantial economic potential. However, the integration of AI with digital vegetation analysis remains underexplored, largely due to the absence of large-scale, real-world plant datasets, which are crucial for advancing this field. To address this gap, we introduce the PP3D dataset—a meticulously labeled collection of about 500 potted plants represented as 3D point clouds, featuring fine-grained annotations for approximately 20 species. The PP3D dataset provides 3D phenotypic data for about 20 plant species spanning model organisms (e.g., Arabidopsis thaliana), potted plants (e.g., Foliage plants, Flowering plants), and horticultural plants (e.g., Solanum lycopersicum), covering most of the common important plant species. Leveraging this dataset, we propose the panoptic plant recognition task, which combines semantic segmentation (stems and leaves) with leaf instance segmentation. To tackle this challenge, we present SCNet, a novel dual-representation learning network designed specifically for plant point cloud segmentation. SCNet integrates two key branches: a cylindrical feature extraction branch for robust spatial encoding and a sequential slice feature extraction branch for detailed structural analysis. By efficiently propagating features between these representations, SCNet achieves superior flexibility and computational efficiency, establishing a new baseline for panoptic plant recognition and paving the way for future AI-driven research in plant science. Full article

The construction of stochastic models and the use of stochastic simulations for their analysis constitute research methods used in the analysis of stochastic processes. These methods can be applied to processes carried out in underground mines. For mining processes, carried out, e.g., in hard coal mines, it is noticed that the influence of factors generally referred to as geological–mining and technical–organizational may cause a given process to be treated as not fully defined (not necessarily in terms of the process technology, but, e.g., taking into account the time taken for its implementation). This article draws attention to the possibilities of reducing the stochastic model based on the use of the properties of the convolution of probability density functions. Functions that were considered appropriate for describing the processes discussed based on time-based studies were presented. The mechanism of operation of the proposed model reduction was discussed. Reduction carried out in this way eliminates the need to analyze these parts of the model, e.g., using the stochastic simulation method. This reduction leads to simplification of the model and the calculations, which translates into the effectiveness of the research. Full article

Background and Objectives: Total hip arthroplasty (THA) following pelvic osteotomy for developmental dysplasia of the hip (DDH) is technically challenging due to altered acetabular morphology. This study aimed to compare radiographic cup position and perioperative characteristics of THA after three common pelvic osteotomies—periacetabular osteotomy (PAO), shelf procedure, and Chiari osteotomy—with primary THA in Crowe type I DDH. Methods: A retrospective review identified 25 hips that underwent conversion THA after pelvic osteotomy (PAO = 12, shelf = 8, Chiari = 5) and 25 primary THAs without prior osteotomy. One-to-one matching was performed based on sex (exact match), age (within 5 years), and BMI (within 2 kg/m²) without the use of propensity scores. Cup inclination, radiographic anteversion, center-edge (CE) angle, and cup height were measured on standardized anteroposterior radiographs (ICC = 0.91). Operative time, estimated blood loss, and use of bulk bone grafts or reinforcement rings were reviewed. One-way ANOVA with Dunnett’s post hoc test and chi-square test were used for statistical comparison. Results: Cup inclination, anteversion, and CE angle did not differ significantly among groups. Cup height was significantly greater in the PAO group than in controls (29.0 mm vs. 21.8 mm; p = 0.0075), indicating a more proximal hip center. The Chiari and shelf groups showed upward trends, though not significant. Mean operative time tended to be longer after PAO (123 min vs. 93 min; p = 0.078). Bulk bone grafts and reinforcement rings were more frequently required in the PAO group (17%; p = 0.036 vs. control), and occasionally in Chiari cases, but not in shelf or control groups. Conclusions: THA after PAO is associated with higher cup placement and greater need for reconstructive devices, indicating increased technical complexity. In contrast, shelf and Chiari conversions more closely resemble primary THA. Preoperative planning should consider hip center translation and bone-stock restoration in post-osteotomy THA. Full article

This research is concerned with design optimization of control systems. Our case study deals with magnetic levitation, in which an essential part is a solenoid. Its dimensions, along with controller parameters, form the optimization variables. We present a novel way of writing the explicit expression of the solenoid’s force acting on a magnetic dipole, as well as its first derivatives. Numerical tests using non-gradient search algorithms show the difference in optimal designs provided by these methods. Since such optimization depends on output signals, a comparison of step response analysis methods is presented. Full article

The plant-based oil industry contributes significantly to food waste/by-products in the form of underutilized biomass, including oil pomace, cake/meal, seeds, peels, wastewater, etc. These waste/by-products contain a significant quantity of nutritious and bioactive compounds (phenolics, lignans, flavonoids, dietary fiber, proteins, and essential minerals) with proven health-promoting effects. The utilization of them as natural, cost-effective, and food-grade functional ingredients in novel food formulations holds considerable potential. This review highlights the potential of waste/by-products generated during plant-based oil processing as a promising source of bioactive compounds and covers systematic research, including recent studies focusing on innovative extraction and processing techniques. It also sheds light on their promising potential for valorization as food ingredients, with a focus on specific examples of food fortification. Furthermore, the potential for value creation in the food industry is emphasized, taking into account associated challenges and limitations, as well as future perspectives. Overall, the current information suggests that the valorization of plant-based oil industry waste and by-products for use in the food industry could substantially reduce malnutrition and poverty, generate favorable health outcomes, mitigate environmental concerns, and enhance economic profit in a sustainable way by developing health-promoting, environmentally sustainable food systems. Full article