Search Results (2,226)

In the field of electromagnetic design, it is sometimes necessary to search for the optimal design solution (i.e., the optimal solution) within a large solution space to complete the optimization. However, traditional optimization methods are not only slow in searching for the solution space but are also prone to becoming trapped in local optima, leading to optimization failure. This paper proposes a dual-population genetic algorithm to quickly find the optimal solution for electromagnetic optimization problems in large solution spaces. The method involves two populations: the first population uses the powerful dynamic decision-making ability of reinforcement learning to adjust the crossover probability, making the optimization process more stable and enhancing the global optimization capability of the algorithm. The second population accelerates the convergence speed of the algorithm by employing a “leader dominance” mechanism, allowing the population to quickly approach the optimal solution. The two populations are integrated through an immigration operator, improving optimization efficiency. The effectiveness of the proposed method is demonstrated through the optimization design of an electromagnetic metasurface material. Furthermore, the method designed in this paper is not limited to the electromagnetic field and has practical value in other engineering optimization areas, such as vehicle routing optimization, energy system optimization, and fluid dynamics optimization, etc. Full article

The welded beam design problem represents a real-world engineering challenge in structural optimization. The objective is to determine the optimal dimensions of a steel beam and weld length to minimize cost while satisfying constraints related to shear stress ($\tau$), bending stress ($\sigma$), critical buckling load ($Pc$), end deflection ($\delta$), and side constraints. The structural analysis of this problem involves the following four design variables: weld height ($x_1$), weld length ($x_2$), beam thickness ($x_3$), and beam width ($x_4$), which are commonly denoted in structural engineering as $h,l,t,b$ respectively. The structural formulation of this problem leads to a nonlinear objective function, which is subject to five nonlinear and two linear inequality constraints. The optimal solution lies on the boundary of the feasible region, with a very small feasible-to-search-space ratio, making it a highly challenging problem for classical optimization algorithms. This paper explores the application of quantum computing to solve the welded beam optimization problem, utilizing the unique properties of quantum computers for constrained optimization in engineering problems. Specifically, we employ the D-Wave quantum computing system, which utilizes quantum annealing and is particularly well-suited for solving constrained optimization problems. The study presents a detailed formulation of the problem in a format compatible with the D-Wave system, ensuring the efficient encoding of constraints and objective functions. Furthermore, we analyze the performance of quantum computing in solving this problem and compare the obtained results with classical optimization methods. The effectiveness of quantum computing is evaluated in terms of computational efficiency, accuracy, and its ability to navigate complex, constrained search spaces. This research highlights the potential of quantum algorithms in tackling real-world engineering optimization problems and discusses the challenges and limitations of current quantum hardware in solving practical industrial application issues. Full article

Road horizontal curves, which significantly influence drivers’ visual search behavior and are closely linked to traffic safety, also constitute a crucial factor in sustainable road traffic development. This paper uses simulation driving experiments to explore the dynamic response characteristics of 27 typical subject drivers’ visual search behavior regarding road horizontal curve radius. Results show that in a monotonous, open road environment, the driver’s visual search is biased towards the inside of the curve; as the radius increases, the 85th percentile value of the longitudinal visual search length gradually increases, the 85th percentile value of the horizontal search angle gradually decreases, the 85th percentile value of vehicle speed gradually increases, and the dispersion and bias of the gaze points gradually decrease. The search length, horizontal angle, and speed approach the level of straight road sections (380 m, 10° and 115 km/h, respectively). When R ≥ 1200 m, a driver’s dynamic visual search range reaches a stable distribution state that is the same as that of a straight road. A dynamic visual search range distribution model for drivers on straight and horizontal curved road sections is constructed. Based on psychological knowledge such as attention resource theory and eye–mind theory, a human factor engineering explanation was provided for drivers’ attention distribution and speed selection mechanism on road horizontal curve sections. The research results can provide theoretical references for the optimization design of road traffic, decision support to improve the driver training system, and a theoretical basis for determining the visual search characteristics of human drivers in autonomous driving technology, thereby promoting the safe and sustainable development of road traffic. Full article

Human communication has long relied on visual media for interaction, and is facilitated by electronic devices that access visual data. Traditionally, this exchange was unidirectional, constrained to text-based queries. However, advancements in human–computer interaction have introduced technologies like reverse image search and large language models (LLMs), enabling both textual and visual queries. These innovations are particularly valuable in Cultural Heritage applications, such as connecting tourists with point-of-interest recognition systems during city visits. This paper investigates the use of various Vision Language Models (VLMs) for Cultural Heritage visual question aswering, including Bing’s search engine with GPT-4 and open models such as Qwen2-VL and Pixtral. Twenty Italian landmarks were selected for the study, including the Colosseum, Milan Cathedral, and Michelangelo’s David. For each landmark, two images were chosen: one from Wikipedia and another from a scientific database or private collection. These images were input into each VLM with textual queries regarding their content. We studied the quality of the responses in terms of their completeness, assessing the impact of various levels of detail in the queries. Additionally, we explored the effect of language (English vs. Italian) on the models’ ability to provide accurate answers. Our findings indicate that larger models, such as Qwen2-VL and Bing+ChatGPT-4, which are trained on multilingual datasets, perform better in both English and Italian. Iconic landmarks like the Colosseum and Florence’s Duomo are easily recognized, and providing context (e.g., the city) improves identification accuracy. Surprisingly, the Wikimedia dataset did not perform as expected, with varying results across models. Open models like Qwen2-VL, which can run on consumer workstations, showed performance similar to larger models. While the algorithms demonstrated strong results, they also generated occasional hallucinated responses, highlighting the need for ongoing refinement of AI systems for Cultural Heritage applications. Full article

Intelligent optimization algorithms are crucial for solving complex engineering problems. The Parrot Optimization (PO) algorithm shows potential but has issues like local-optimum trapping and slow convergence. This study presents the Chaotic–Gaussian–Barycenter Parrot Optimization (CGBPO), a modified PO algorithm. CGBPO addresses these problems in three ways: using chaotic logistic mapping for random initialization to boost population diversity, applying Gaussian mutation to updated individual positions to avoid premature local-optimum convergence, and integrating a barycenter opposition-based learning strategy during iterations to expand the search space. Evaluated on the CEC2017 and CEC2022 benchmark suites against seven other algorithms, CGBPO outperforms them in convergence speed, solution accuracy, and stability. When applied to two practical engineering problems, CGBPO demonstrates superior adaptability and robustness. In an indoor visible light positioning simulation, CGBPO’s estimated positions are closer to the actual ones compared to PO, with the best coverage and smallest average error. Full article

The current unmanned surface vehicle (USV) intelligent obstacle avoidance algorithm based on deep reinforcement learning usually adopts the mass point model to train in an ideal environment. However, in actual navigation, due to the influence of the ship model and the water surface environment, the training set is triggered. The reward function does not match the actual situation, resulting in a poor obstacle avoidance effect. In response to the above problems, this paper proposes a long and short memory network-proximal strategy optimization (LSTM-PPO) intelligent obstacle avoidance algorithm for non-particle models in non-ideal environments, and designs a corresponding deep reinforcement learning training environment. We integrate the motion characteristics of the unmanned boat and the influencing factors of the surface environment, based on the curiosity-driven set reward function, to improve its autonomous obstacle avoidance ability, combined with the LSTM network to identify and save obstacle information to improve the adaptability to the unknown environment; virtual simulation is performed in Unity. The engine builds a USV physical model and a refined water deep reinforcement learning training environment including a variety of obstacle models. The experimental results demonstrate that the LSTM-PPO algorithm exhibits an effective and rational obstacle avoidance effect, with a success rate of 86.7%, an average path length of 198.52 m, and a convergence time of 1.5 h. A comparison with the performance of three other deep reinforcement learning algorithms reveals that the LSTM-PPO algorithm exhibits a 21.5% reduction in average convergence time, an 18.5% reduction in average path length, and an approximately 20% enhancement in the success rate of obstacle avoidance in complex environments. These results indicate that the LSTM-PPO algorithm can effectively enhance the search efficiency and optimize the path planning in obstacle avoidance for unmanned boats, rendering it more rational. Full article

To address the problem of UAV path planning in complex mountainous terrains, this paper comprehensively considers constraints such as natural mountain and obstacle collision threats, the shortest path, and flight altitude. We propose a more practical UAV path planning model that better reflects the actual UAV path planning situation in complex mountainous areas. In order to solve this model, this paper improves the traditional dung beetle optimization (DBO) algorithm and proposes an improved dung beetle optimization (IDBO) algorithm. The IDBO algorithm optimizes the population initialization method based on the concept of symmetry, ensuring that the population is more evenly distributed within the solution space. Additionally, the algorithm introduces a sine–cosine function-based movement strategy, inspired by the symmetry principle, to enhance the search efficiency of individual population members. Furthermore, a population evolution strategy is incorporated to prevent the algorithm from getting stuck in local optima. To demonstrate the algorithm’s performance, tests were conducted using 23 commonly used benchmark functions provided by the CEC 2005 competition and six commonly used engineering problem models provided by the CEC 2020 competition. The results indicate that IDBO significantly outperforms DBO in terms of convergence performance, effectively solving various engineering optimization problems. Finally, experimental tests under three different threat scenarios show that the proposed IDBO algorithm has scientific validity when applied to UAV path planning. This solution method effectively reduces UAV flight energy consumption costs and obstacle collision threats while improving the efficiency and accuracy of UAV path planning. Full article

Pregnancy involves extracellular vesicles (EVs) through mechanisms that are poorly understood to date. Furthermore, it is not surprising that EVs may also be involved in the pathophysiology of pre-eclampsia (PE) and gestational hypertension, two clinical conditions with high morbidity and mortality, given their capacity to mediate intracellular communications and regulate inflammation and angiogenesis. We searched major online scientific search engines (PubMed, Google Scholar, Scopus, WES, Embase, etc.) using the terms “Preeclampsia”, “Pregnancy”, “Hypertension”, “Pregnancy-related hypertension”, “Extracellular vesicles”, “Biomarkers”, “Gestation” AND “Obstetrics”. Finding potential early biomarkers of risk or illness progression would be essential for the optimum care of expectant mothers with the aforementioned conditions. Nevertheless, none of the various screening assays that have been discovered recently have shown high predictive values. The analysis of EVs in the peripheral blood starting from the first trimester of pregnancy may hold great promise for the possible correlation with gestational hypertension problems and represent a marker of the early stages of the disease. EVs use may be a novel therapeutic approach for the management of various illnesses, as well. In order to define EVs’ function in the physiopathology of pregnancy-associated hypertension and PE, as well as their potential as early biomarkers and therapeutic tools, we have compiled the most recent data in this review. Full article

The surgical procedures in the treatment of periodontitis generally aim to reduce pocket depth, improve teeth maintenance, and enhance long-term stability. Several approaches have been proposed over the years including different techniques, drugs, and medical devices, with the main purpose to minimalize the surgical procedure and improve both the outcomes and patient compliance. Among all the medical devices proposed in surgical and non-surgical periodontology, different laser wavelengths (e.g., Erbium: YAG, Erbium: CR, KTP, Diode) have been studied worldwide but generally with inconsistent results. Nowadays, the diode laser is one of the most used lasers among general dentists, also promoted as an adjunctive therapy in periodontology, mainly in non-surgical procedures. This study aims to review data emerging from the literature on the use of diode lasers and adjunctive therapy to the conventional periodontal surgery. This research was conducted using PubMed and Scopus search engines with a combination of relevant keywords, including “Surgical Periodontal Therapy”, “Periodontal Surgery”, “Periodontal Regenerative Surgery”, and “Surgical Treatment for Periodontal Disease” in combination with “Diode Laser”, “Diode-Driven Laser”, and “Diode Emission Laser”. Selected articles were carefully reviewed to identify studies reporting data on the effectiveness of diode lasers in periodontal surgery. Results of the current review suggest potential clinical benefits of diode laser-assisted periodontal surgery, as studies reported improvements in key parameters such as clinical attachment level (CAL), bleeding on probing (BOP), and plaque index (PI) postoperatively. Additionally, VAS scores, indicative of post-surgical discomfort, were lower for laser-treated sites, and a short-term reduction in periodontal pathogens was observed. However, the clinical outcomes significantly differ across the studies, and the evidence does not uniformly support a superior effectiveness of diode lasers as an adjunctive tool in surgical periodontology. While the data indicate that diode laser use may contribute to enhanced periodontal health and possibly accelerate healing in some cases, these findings should be interpreted cautiously, as further research, possibly multicentric and in large numbers but mandatory with well-defined protocols (stage of periodontitis, preliminary non-surgical procedures and results, laser wavelength and protocol of use, post-operative maintenance, follow-up clinical and radiological criteria) are surely needed to possibly validate the observations emerging from the current review and eventually to standardize clinical protocols in the future. Such limitations have been well addressed in this paper and are clearly discussed and essentially related to the focus on the total uncertainty of the literature and general caution. Full article

Background/Objectives: Zygomaticomaxillary complex (ZMC) fractures are the second most common of all facial bone fractures, and conventional treatment represents a challenge even for experienced surgeons. The aim of this systematic review and meta-analysis was to compare Intraoperative Surgical Navigation (ISN) with conventional surgery in the treatment of ZMC fractures. Methods: We reported our systematic review and meta-analysis based on the recommendation of the PRISMA 2020 guideline. The electronic search was conducted on 9 February 2025 in two search engines (PubMed and Web of Science) and two databases (Embase and the Cochrane Library). Randomized controlled trials and observational studies were included. The outcome variables were accuracy, operative time, maximum mouth opening, postoperative stay, amount of bleeding, and cheek numbness. The random-effects model was used for the analysis, and the results were given as mean differences and odds ratios with 95% confidence intervals (CI). After duplicate removal, 1961 articles were found. After the selection procedure, five studies were found to be eligible for qualitative and quantitative analysis. Results: There were no statistically significant differences between ISN and conventional surgery regarding the outcomes investigated, except in postoperative average deviation of the zygomatic bone. Our results showed an improvement of 0.64 mm [CI: 0.32, 0.92] zygomatic bone deviation when ISN was used. Conclusions: The results suggest that ISN is as effective as the conventional technique in the treatment of ZMC fractures. However, because of the low number of eligible studies, further randomized controlled trials are necessary to strengthen the level of evidence on this matter. Full article

With the rising frequency and severity of wildfires that cause significant threats to ecosystems, public health and livelihoods, it is essential to have tools for evaluating and monitoring their impacts and the effectiveness of policy initiatives. This paper presents the development and implementation of a new calculation pipeline integrated with a web-based platform designed to provide georeferenced data on the burn severity of wildfires in mainland Portugal. The platform integrates a modular architecture that comprises a module in R and Google Earth Engine to compute standardized satellite-derived datasets on observed/historical severity for burned areas, integrated with a web portal module to facilitate the access, search, visualization, and downloading of the generated data. The platform provides open-access, multisource data from satellite missions, including MODIS, Landsat-5, -7, and -8, and Sentinel-2. It offers multitemporal burn severity products, covering up to 12 months post-fire, and incorporates three severity indicators, the delta NBR, relative difference NBR, and relativized burn ratio, derived from Normalized Burn Ratio (NBR) quarterly median composites. The platform’s modular and scalable framework also allows the integration of more spectral indices, burn severity indicators, and other wildfire perimeter databases. These design features also enable the platform to adapt to other contexts or regions beyond its current scope and regularly update burn severity products. Results from exploratory data analyses revealed the ability of satellite-based severity products to diagnose trends, assess interannual variability, and enable regional comparisons of burn severity, providing a basis for further research. In the face of climate change and societal challenges, the platform aims to support decision-making processes by providing authorities with standardized and updated information while promoting public awareness of wildfire challenges and, ultimately, contributing to the sustainability of rural landscapes. Full article

Monitoring the behavior of structures over time is a defining activity for any type of construction that provides data necessary to assess whether these structures meet requirements for stability and durability. In today’s rapidly urbanizing world, it is essential to monitor construction projects. Whether monitoring the impact on buildings surrounded by new constructions, underground infrastructure, or high-rise structure projects, the solutions and results provided by the construction monitoring process, carried out during the execution phase and/or operational stage, enable communities to progress and thrive without jeopardizing people and assets. This study aimed to highlight the level of interest in structural monitoring activities. A bibliometric analysis based on scientific articles published in the most popular databases brings to the forefront correlations and links between various fields of activity and the domain of construction monitoring through the application of various technologies. These published studies and the centralization of the number of searches for specialized terms related to structural health monitoring activities present a combination of classical theories with modern technologies that have evolved rapidly due to the continuous development of civil and geodetic engineering technology, as well as the introduction of artificial intelligence in interpreting recorded observations. This research results show that this topic is relevant and increasingly studied; for example, the number of scientific articles published on this subject doubled in the last three years compared to previous years. According to the literature, research trends are focused on new technologies, including the application of various sensor types, UAV technology, and LiDAR. The number of publications showed an increased interest in the study, monitoring, and evaluation of bridges, followed by research on civil constructions. Among civil constructions, aging or special buildings were most frequently encountered, while new structures accounted for a smaller percentage according to scientific articles published in the specialized literature. Full article

This paper proposes a novel optimization method for fault current limiter (FCL) reactance configuration based on joint simulation and penalty function constraint optimization. By integrating MATLAB and ATP for joint simulation, the method accurately derives the constraint conditions of the objective optimization function, providing critical data support for the optimization process. To address the challenges of high computational complexity and solution difficulties in constrained optimization, the Penalty Function Method (PFM) is employed to transform the original constrained optimization problem into a standard unconstrained optimization problem, significantly reducing computational complexity and ensuring the feasibility of the solution. On this basis, the Gravitational Search Algorithm (GSA) is applied to compute the optimal reactance value. Through comparative analysis of engineering case studies, the superiority of the GSA over the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in optimization performance is validated, further confirming the accuracy and efficiency of the proposed method. The results indicate that this method not only achieves precise calculation results but also significantly improves computational efficiency. Moreover, the integration of PFM and GSA demonstrates excellent robustness, providing reliable technical support for the optimized deployment of fast-switching fault current limiters in large-scale power grids. Full article

Throughout its service life, an aero-engine will experience a series of health conditions due to the inevitable performance degradation of its major components, and characteristics will deviate from their initial states. For improving tracking accuracy of the self-tunning on-board engine model on the engine output variables throughout the engine service life, a new method based on the separability index and reverse search algorithm was proposed in this paper. By using this method, a qualified training set of neural networks was created on the basis of eSTORM (enhanced Self Tuning On-board Real-time Model) database, and the problem that the accuracy of neural networks is reduced or even that the training process is not convergent can be solved. Compared with the method of introducing sample memory factors, the method proposed in this paper makes the self-tunning on-board model maintain higher tracking accuracy in the whole engine life, and the algorithm is simple enough for implementation. Finally, the training set center generated in the calculation process of the proposed method could be used for the real-time monitoring of the engine gas path parameters without additional calculations. Compared with the commonly used sliding window method, the proposed method avoids the problem of low algorithm efficiency caused by fewer abnormal data samples. Full article

Background/Objectives: Tumour-associated macrophages (TAMs) are critical components of the tumour microenvironment (TME), significantly influencing cancer progression and treatment resistance. This review aims to explore the innovative use of engineered bacteria to reprogram TAMs, enhancing their anti-tumour functions and improving therapeutic outcomes. Methods: We conducted a systematic review following a predefined protocol. Multiple databases were searched to identify relevant studies on TAMs, their phenotypic plasticity, and the use of engineered bacteria for reprogramming. Inclusion and exclusion criteria were applied to select studies, and data were extracted using standardised forms. Data synthesis was performed to summarise the findings, focusing on the mechanisms and therapeutic benefits of using non-pathogenic bacteria to modify TAMs. Results: The review summarises the findings that engineered bacteria can selectively target TAMs, promoting a shift from the tumour-promoting M2 phenotype to the tumour-fighting M1 phenotype. This reprogramming enhances pro-inflammatory responses and anti-tumour activity within the TME. Evidence from various studies indicates significant tumour regression and improved immune responses following bacterial therapy. Conclusions: Reprogramming TAMs using engineered bacteria presents a promising strategy for cancer therapy. This approach leverages the natural targeting abilities of bacteria to modify TAMs directly within the tumour, potentially improving patient outcomes and offering new insights into immune-based cancer treatments. Further research is needed to optimise these methods and assess their clinical applicability. Full article