Applied Sciences

The complex nonlinear behaviour of unreinforced masonry (URM), along with the interaction between structural elements, still represents a challenge for the seismic assessment of existing URM buildings. A large variety of mathematical tools have been developed in the last decades to address the issue. The numerical work herein presented attempts to provide some insights into the use of FEM models to obtain reliable results from nonlinear dynamic analyses conducted with explicit methods. Through plane stress elements, two in-plane mechanisms were studied to identify optimal parameters for unreinforced masonry elements subjected to dynamic actions. The results were then compared with outcomes generated by an implicit solver. Subsequently, these parameters were used in nonlinear dynamic analyses on a building section for the seismic assessment in both unreinforced and reinforced conditions. The element type, hourglass control, damping, and bulk viscosity influence the dynamic response, mainly when the nonlinearities become larger. The hourglass control techniques employ a scaling factor to suppress the occurrence of spurious modes. Values ranging from 0.01 to 0.03 have shown effective results. When the stiffness-damping parameter for Rayleigh damping is of a similar order of magnitude or lower than the time increment without damping, the time increment remained in feasible ranges for performing analysis. Additionally, the bulk viscosity can stabilise the response without causing substantial alterations to the time increment if the values are under 1.00. Full article

This paper proposes a Field Programmable Gate Array (FPGA)-based control system to implement micro-light-emitting diode (micro-LED) real-time display. The control system includes the interface control, video processing, memory management, image data transmission, control signal generation and correction. Then, we implement the micro-LED real-time display via memory management. We propose the brightness correction to achieve high grey-scale and high uniformity display. The LEDs are mounted on the glass substrate prepared using low-temperature polysilicon (LTPS) technology, and then we find the 24 × 46 pixels micro-LED panel. And the control system has been successfully applied to the panel of glass-based micro-LED displays. A new grey control method is proposed in this work, which can effectively improve the refresh rate of the micro-LED displays. The high grey-scale refresh rate is 2100 Hz, and the low grey-scale refresh rate is 300 Hz. The uniformity of the panel is increased to 85% after brightness correction. Full article

Objective: The aims of this study were (1) to determine the one-year prevalence of injuries and their characteristics and (2) to analyze potential injury risk factors in high-level/national-level women artistic gymnasts. Methods: Competitive women artistic gymnasts training more than 10 h per week in a national training center and a TOP 12 club in France were invited to participate in a retrospective study through an online questionnaire about their self-reported injuries and potential injury risk factors (e.g., previous injuries, diseases, weight modification) during the 2020/2021 season. Results: A total of 88 gymnasts between 9 and 23 years old, who trained on average 22.0 ± 6.2 h per week, answered the questionnaire. Ninety-five injuries were reported by 62 (70.5%) of the 88 gymnasts. The one-year injury prevalence was 70.5% (95% CI 60.9 to 80.0%). Gymnasts had, on average, 1.1 ± 1.0 injuries per year. The most common reported injury location was the knee (25.3%), and the most common reported injury type was the ligament (26.3%). Having an injury during the previous season (OR = 9.06; CI 95% 2.66 to 36.73; p = 0.0007) and using a prevention program (OR = 3.97; CI 95% 1.46 to 11.15; p = 0.007) were associated with a higher risk of injury in the multivariate analysis. Conclusions: More than 70% of French high-level/national-level gymnasts had at least one injury during the 2020/2021 season. This high injury rate supports the need to promote injury prevention. However, in the present study, using a prevention program was associated with a higher risk of injury. There is thus a need to improve injury prevention strategies to counter the high injury risk and its potential consequences. Full article

Construction accidents can lead to serious consequences. To reduce the occurrence of such accidents and strengthen the execution capabilities in on-site safety management, managers must analyze accident report texts in depth and extract valuable information from them. However, accident report texts are usually presented in unstructured or semi-structured forms; analyzing these texts manually requires a lot of time and effort, it is difficult to cope with the demand of analyzing a large number of accident texts, and the quality of key information extracted manually may be poor. Therefore, this study proposes a classification method based on natural language processing (NLP) technology. First, we developed a text classification model based on a convolutional neural network (CNN) that can automatically classify accident categories based on accident text features. Next, taking the classified fall accidents as an example, we extracted key information from accident narratives using the term frequency-inverse document frequency (TF-IDF) method and presented it visually using word clouds. The results show that the overall accuracy of the CNN model reaches 84%, which is better than the other three shallow machine-learning models. Then, eight key accident areas and three accident-prone operations were identified using the TF-IDF algorithm. This study can provide important guidance for project managers and can be used for on-site safety management to help prevent production safety accidents. Full article

Animals utilize their surroundings to make decisions on how to navigate and establish their territories. Some species gather information about competing groups by observing them from a distance, detecting scent markings, or relying on memories of encounters with rival populations. Gathering such information involves a nonlocal process, prompting the development of mechanistic models that incorporate nonlocal terms to explore species movement. These models, however, pose analytical and computational challenges. In this study, we focus on a multi-species advection–diffusion model that incorporates nonlocal advection. To efficiently compute solutions for this system involving a large number of interacting species, we introduce a numerical scheme using spectral methods. Additionally, we examine the influence of various parameters and interaction potentials on population densities. Our investigation aims to provide a method to identify the primary factors driving species movements, and we validate our approach using synthetic data. Full article

Amidst the evolving communication technology landscape, conventional distribution networks have gradually metamorphosed into cyber–physical power systems (CPPSs). Within this transformative milieu, the cyber infrastructure not only bolsters grid security but also introduces a novel security peril—the false data injection attack (FDIA). Owing to the variable knowledge held by cyber assailants regarding the system’s network structure, current achievements exhibit deficiencies in accommodating the detection of FDIA across diverse attacker profiles. To address the historical data imbalances encountered during practical FDIA detection, we propose a dataset balancing model based on generating adversarial network-gated recurrent units (GAN-GRU) in conjunction with an FDIA detection model based on the Transformer neural network. Harnessing the temporal data extraction capabilities of gated recurrent units, we construct a GRU neural network system as the GAN’s generator and discriminator, aimed at data balance. After preprocessing, the balanced data are fed into the Transformer neural network for training and output classification to discern distinct FDIA attack types. This model enables precise classification amidst varying FDIA scenarios. Validation involves testing the model on load data from the IEEE 118-bus system and affirming its high accuracy and effectiveness in detecting power systems after multiple attacks. Full article

Although structural topology optimization has been developing for decades, it still plays a leading role within the area of engineering design. Solving contemporary design problems coming from industry requires the implementation of efficient methods and approaches. This stimulates research progress in the development of novel and versatile topology optimization algorithms. To follow these modern trends, an original topology generator has been elaborated and finally built as a Cellular Automaton with original update rules. The motivation for building the algorithm in this way came from the idea of utilizing the benefits of local compliances sorting. This is conducted on two levels: on the global level, the monotonic function mapping local compliances distribution is defined based on their sorted values; on the local level, for each cell, the compliances are sorted within the cell neighborhood. The three largest absolute values are selected, and these are the basis from which to formulate Cellular Automata update rules. These original rules can efficiently control the generation of structural topologies. This technique is somewhat inspired by the grey wolf optimizer strategy, wherein the process of updating design variables refers to the positions of the three best fitted wolves. It is proposed that we refer to the topology algorithm that benefits from the adaptation of sorted compliances optimization as TABASCO. The developed algorithm is a modified version of the flexible Cellular Automata one presented previously. The implemented extension, regarding the local level cell sorting, allows us to improve the resulting compliance values. The advantages of the algorithm, both from numerical and practical engineering points of view, as compared to the others developed within the field, may be gathered as follows: the algorithm works based on simple update rules, i.e., its numerical implementation is not complicated; it does not require gradient computations; filtering techniques are not needed; and it can easily be combined with professional structural analysis programs which allow engineering applications. The developed topology generator has been linked with ANSYS to show that it can be incorporated into a commercial structural analysis package. This is especially important with respect to the engineering implementations. Full article

Recently, hydrogen (H₂) energy has attracted attention among eco-friendly energy sources because H₂ energy is eco-friendly, energy-efficient, and abundant in nature. However, when the concentration of H₂ in the atmosphere is more than 4%, H₂ has a risk of explosion. H₂ is a colorless, tasteless, and odorless gas that is difficult to detect with human senses. Therefore, developing an optimized hydrogen sensor is essential. Palladium (Pd) has good reactivity to hydrogen. Molybdenum disulfide (MoS₂) has high carrier mobility, sensitive reactivity to toxic gases, and high surface-area-to-volume ratio. Therefore, we proposed hydrogen sensors that use Pd and MoS₂. The main fabrication processes include MoS₂ deposition through CVD and Pd deposition through DC sputtering. In this study, we utilized Pd and MoS₂ to enable sensing at room temperature. By optimizing the Pd to a nanoparticle structure with an expansive surface area of 4 nm, we achieved a fast response time of 4–5 s and an enhanced yield through a simplified structure. Hydrogen sensors inherently exhibit sensitivity to various environmental factors. To address these challenges, technologies such as machine learning can be incorporated. Emphasizing low-power consumption and various application compatibilities becomes pivotal to promoting commercialization across diverse industries. Full article

As a semi-arid Mediterranean country, Tunisia is affected by the impacts of climate change, particularly the coastal regions like the Cap-Bon. Irregular rainfall, rising temperatures and the recurrence of extreme events are all indicators that affect ecosystems and populations and make them more vulnerable to the influence of climatic variables. Therefore, an analysis of the trends of climate variables can contribute to facilitating the development of effective adaptation strategies. In this matter, this study was conducted to assess the homogeneity and trends of minimum and maximum air temperature (Tmin and Tmax) and precipitation (P) in the Cap-Bon region. Daily data were collected from the meteorological station of Nabeul for the period of 1982–2020. Pettitt and SNHT tests for homogeneity were applied to identify the breakpoints in multi-time scales of Tmax, Tmin and P data series. The Mann–Kendall (MK) test was used to detect the change in the time-series trend. A modified Mann–Kendall (mMK) test was used to remove the autocorrelation effect from the data series. Both the MK and mMK tests were used at the 5% significant level. The magnitude of the climatic trend was estimated using the non-parametric Sen’s slope estimator. Contrary to Tmin and P, the results of the homogeneity tests revealed the existence of significant breakpoints in the annual, seasonal and monthly Tmax time series. For most cases, the breakpoint occurred around the year 2000. For Tmin, significant breakpoints were recorded in March and April, while a significant shift in the P time series was detected in December. The Mann–Kendall results show a significant warming trend in annual Tmax, with magnitudes equal to 0.065 and 0.045 °C/year before and after the breakpoint, respectively. Nevertheless, non-significant tendencies were observed in the annual Tmin and P time series. On the monthly time scale, Tmax exhibited a significant upward trend in June and August, before the observed breakpoints, with Sen’s slope values equal to 0.065 and 0.045 °C/year, respectively. Regarding the Tmin data, a significant positive trend was observed in July at a rate of 0.033 °C/year. Full article

Claystone is potentially the main rock formation for the deep geological disposal of high-level radioactive nuclear waste. A major factor that affects the deformation of the host medium is effective stress. Therefore, studying the effective stress principle of claystone is essential for a stability analysis of waste disposal facilities. Consolidated drained (CD) tests were carried out on claystone samples to study their effective stress principle in this paper. Firstly, two samples were saturated under a specified confining pressure and pore pressure for about one month. Secondly, the confining pressure and pore pressure were increased to a specified value simultaneously and then reverted to the previous stress state (the deformations of the samples were recorded during the whole process). Different incremental combinations of the confining pressure and pore pressure were tried at this step. Finally, the effective stress coefficients of the samples were obtained through a back analysis. Furthermore, some potential influencing factors (the neutral stress and loading rate) of the effective stress coefficient were also studied through additional tests. Some interesting results are worth mentioning: (1) the effective stress coefficient of claystone is close to one; (2) the neutral stress and loading rate may have little effect on the effective stress coefficient of claystone. Full article

Thermally synthesized graphitic carbon nitride (g-C₃N₄) over pulsed laser deposition (PLD) produced urchin-like iron oxide (FeOx) thin films were fabricated via in situ and ex situ processes. Materials characterisation revealed the formation of the graphitic allotrope of C₃N₄ and a bandgap E_g for the combined FeOx/g-C₃N₄ of 1.87 and 1.95 eV for each of the different fabrication strategies. The in situ method permitted to develop a novel petal-like morphology, whereas for the ex situ method, a morphological mixture between FeOx bulk and g-C3N4 was observed. Given the improved optical and morphological properties of the in situ film, it was employed as a proof of concept for the direct photocatalysis and photo-Fenton removal of ciprofloxacin antibiotic (CIP) under visible light irradiation. Improved photocatalytic activity (rate constant k = 8.28 × 10⁻⁴ min⁻¹) was observed, with further enhancement under photo-Fenton conditions (k = 2.6 × 10⁻³ min⁻¹), in comparison with FeOx + H₂O₂ (k = 1.6 × 10⁻³min⁻¹) and H₂O₂ only (k = 1.3 × 10⁻⁴ min⁻¹). These effects demonstrate the in situ methodology as a viable route to obtain working heterojunctions for solar photocatalysis in thin-film materials, rather than the more common powder materials. Full article

Chirality, the absence of mirror symmetry, is predominant in nature. The chiral nature of the electromagnetic field behaves differently with chiral matter for left circularly polarized and right circularly polarized light. The chiroptical behavior in the sensing of naturally occurring chiral objects is weak, and improving the chiroptical response enhances the chiral sensing platform. This review covers the fundamental concepts of chiral metasurfaces and various types of single- and multi-layered chiral metasurfaces. In addition, we discuss tunable and deep-learning-based chiral metasurfaces. Tunability is achieved by manipulating the meta-atom’s property in response to external stimuli for applications such as optical modulation, chiral photonics, advanced sensing, and adaptive optics. Deep-learning modeling techniques, such as CNNs and GANs, offer efficient learning of the complex relationships in data, enabling the optimization and accurate prediction of chiral metasurface properties. The challenges in the design and fabrication of chiral metasurface include achieving broadband performance and scalability and addressing material limitations. Chiral metasurface performance is evaluated by optical rotation, circular dichroism enhancement, and tunability, which are quantified through the spectroscopic measurement of circular dichroism and optical rotation. Chiral metasurface progress enables applications, including metaholography, metalenses, and chiral sensing. Chiral sensing improves the detection of pharmaceuticals and biomolecules, increasing the sensitivity and accuracy of analytical diagnostics. Full article

Low-resource languages often face the problem of insufficient data, which leads to poor quality in machine translation. One approach to address this issue is data augmentation. Data augmentation involves creating new data by transforming existing data through methods such as flipping, cropping, rotating, and adding noise. Traditionally, pseudo-parallel corpora are generated by randomly replacing words in low-resource language machine translation. However, this method can introduce ambiguity, as the same word may have different meanings in different contexts. This study proposes a new approach for low-resource language machine translation, which involves generating pseudo-parallel corpora by replacing phrases. The performance of this approach is compared with other data augmentation methods, and it is observed that combining it with other data augmentation methods further improves performance. To enhance the robustness of the model, R-Drop regularization is also used. R-Drop is an effective method for improving the quality of machine translation. The proposed method was tested on Chinese–Kazakh (Arabic script) translation tasks, resulting in performance improvements of 4.99 and 7.7 for Chinese-to-Kazakh and Kazakh-to-Chinese translations, respectively. By combining the generation of pseudo-parallel corpora through phrase replacement with the application of R-Drop regularization, there is a significant advancement in machine translation performance for low-resource languages. Full article

Artificial intelligence techniques have lately been used to estimate the mechanical properties of concrete to reduce time and financial expenses, but these techniques differ in their processing time and accuracy. This research aims to develop a new procedural binary particle swarm optimization algorithm (NPBPSO) by making some modifications to the binary particle swarm optimization algorithm (BPSO). The new software has been created based on some fresh state properties (slump, temperature, and grade of cement) obtained from several ready-mix concrete plants located in Aleppo, Syria to predict the density and compressive strength of the regional concrete mixtures. The numerical results obtained from NPBPSO have been compared with the results from BPSO and artificial neural network ANN. It has been found that BPSO and NPBPSO are both predicting the compressive strength of concrete with less number of iterations and more accuracy than ANN (0.992 and 0.998 correlation coefficient in BPSO and NPBPSO successively and 0.875 in ANN). In addition, NPBPSO is better than BPSO as it prevents the algorithm from falling into the problem of local solutions and reaches the desired optimal solution faster than BPSO. Moreover, NPBPSO improves the accuracy of obtained compressive strength values and density by 30% and 50% successively. Full article

Semantic information interaction plays an important role in transportation infrastructure modeling and management. To ensure semantic consistency during information exchange and data integration, ontology technology is commonly employed to measure the semantic relevance between concepts. Ontology semantic similarity accurately expresses relationships among various concepts in the domain, and when combined with Building Information Modeling (BIM) technology, it improves the efficiency of information transmission and management in construction. However, the complex structure, diverse components, and strong attribute diversity of transportation infrastructure pose challenges for analysis and computation, leading to limited precision in existing ontology semantic similarity methods. Aimed at these issues, this paper proposes a transport infrastructure ontology concept semantic similarity measurement model based on the Back Propagation (BP) neural network algorithm improved by the Spotted Hyena Optimizer (SHO-BP). Firstly, a semantic network for transportation infrastructure is established, and an ontology-based semantic similarity calculation model is constructed with three approaches, including Edge-Counting method, Feature-based method, and Information-Content method. Then, the SHO-BP algorithm is employed to comprehensively weight the three similarity measure approaches above. Finally, using bridge BIM models as examples, the semantic similarity of transportation infrastructure concepts involved in the BIM models are computed based on the weighted model derived from the aforementioned processes. The experiments demonstrate that the SHO-BP algorithm achieves a higher Pearson correlation coefficient than other algorithms for the comprehensive semantic similarity results in the field of transportation infrastructure. This improvement effectively enhances the accuracy of ontology semantic similarity calculation, and it is conducive to the sharing and integration of BIM information in different systems. Full article

The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues regarding their applicability. A system named Incentivizing Secure Federated Learning Systems (ISFL-Sys) is proposed, consisting of a blockchain module and a federated learning module. A data-security-oriented trustworthy federated learning mechanism called Efficient Trustworthy Federated Learning (ETFL) is introduced in the system. Utilizing a directed acyclic graph as the ledger for edge nodes, an incentive mechanism has been devised through the use of smart contracts to encourage the involvement of edge nodes in federated learning. Experimental simulations have demonstrated the efficient security of the proposed federated learning mechanism. Furthermore, compared to benchmark algorithms, the mechanism showcases improved convergence and accuracy. Full article

With aging being a major non-reversible risk factor for cardiovascular disease, the concept of Vascular Age (VA) emerges as a promising alternate measure to assess an individual’s cardiovascular risk and overall health. This study investigated the use of frequency features and Supervised Learning (SL) models for estimating a VA Age-Group (VAAG), as a surrogate of Chronological Age (CHA). Frequency features offer an accessible alternative to temporal and amplitude features, reducing reliance on high sampling frequencies and complex algorithms. Simulated subjects from One-dimensional models were employed to train SL algorithms, complemented with healthy in vivo subjects. Validation with real-world subject data was emphasized to ensure model applicability, using well-known risk factors as a form of cardiovascular health analysis and verification. Random Forest (RF) proved to be the best-performing model, achieving an accuracy/AUC score of 66.5%/0.59 for the in vivo test dataset, and 97.5%/0.99 for the in silico one. This research contributed to preventive medicine strategies, supporting early detection and personalized risk assessment for improved cardiovascular health outcomes across diverse populations. Full article

This study has focused on analyzing the impact of material characteristics and grinding conditions on the surface roughness in ELID grinding of ceramic materials intended for bearing balls. The main research objective was to examine the feasibility of achieving the required surface roughness for G5 class bearing balls through a high-efficiency and high-precision ELID grinding process. Three types of silicon nitride specimens and two types of grinding wheels with cBN and diamond abrasives were prepared for the experiments. An HP (high-pressure) specimen was fabricated through high-temperature and high-pressure sintering at 1700 °C for 2 h, containing a composition of Y₂O₃ and MgO in Si₃N₄, while GPS 1hr and GPS 6hr specimens were prepared using gas-pressure sintering for 1 h and 6 h, respectively. From the experimental results, it has been confirmed through surface morphology and surface roughness analysis that material characteristics and grinding parameters affect the surface roughness of silicon nitride ceramics during the grinding process. The surface ground with a #2000 diamond wheel is at a level that can satisfy the required surface roughness, 0.014 um or less in G5 class bearing balls. Based on the analysis of surface morphology and roughness in grinding processes, the #325 cBN wheel exhibited excellent performance in rough grinding, while the #2000 diamond wheel demonstrated highly effective surface finishing performance, indicating that the combination of these two abrasives can be effectively utilized for high-efficiency and high-precision nanosurface machining of silicon nitride ceramics. Full article