Search Results (69,166)

The accurate measurement of spindle errors, especially quasi-static errors, is one of the key issues for the analysis and compensation of machine tool thermal errors in machining accuracy. To quantitatively analyze the influence of the measurement system’s own drift on the measurement results, a drifted uncertainty evaluation method of the precision instrument considering the time drift coefficient is proposed. This study also produced a high-precision compact spindle error measurement device (with a displacement measurement error of less than ±1.33 μm and an angular measurement error of less than ±1.42 arcsecs) as the research object to verify the proposed drift uncertainty evaluation method. A method for evaluating the drift uncertainty of the measurement system is proposed to quantitatively evaluate the system error and drift uncertainty of the measurement device. Experiments show that the drift uncertainty evaluation method proposed in this paper is more suitable for evaluating the uncertainty changes in measurement instruments during long-term measurements compared to traditional methods. Full article

Moisture-induced damage is a serious problem that severely impairs asphaltic pavement and affects road serviceability. This study examined numerous variables in asphalt concrete mixtures to assess their impact on moisture damage resistance. Mix design parameters such as the asphalt content (AC) and aggregate passing sieve No. 4 (PNo. 4) were considered as variables during this study. Additionally, hydrated lime (HL) was utilized as a partial substitute for limestone dust (LS) filler at 1.5% by weight of the aggregate in asphalt concrete mixtures for the surface layer. This study also investigated the potential enhancement of traditional asphalt binders and mixtures by adding nano-additives, specifically nano-silica oxide (NS) and nano-titanium dioxide (NT), at rates ranging from 0% to 6% by weight of the asphalt binder. To quantify the moisture damage resistance of the asphalt concrete mixes, two types of laboratory tests were employed: the tensile strength ratio (TSR) and the index of retained strength (IRS). The former characterizes moisture damage using tensile strength, whereas the latter uses compression strength. The physical properties of the asphalt binder, such as its penetration, softening point, and ductility, were also evaluated to identify the effects of the nanomaterials. The results indicated that variations in the mix design variables significantly affected the moisture damage resistance of the asphalt concrete mixtures. The maximum improvement values were obtained at the optimum asphalt content (OAC) and PNo. 4 (mid-range + 6%) with TSR values of 80.45 and 82.46 and IRS values of 74.39 and 77.14, respectively. Modifying asphalt concrete mixtures with 1.5% HL resulted in improved moisture resistance compared with mixtures without HL (0% HL) at each PNo. 4 level, reaching superior performance at PNo. 4 (mid-range + 6%) by 4.58% and 3.96% in the TSR and IRS tests, respectively. Additionally, both NS and NT enhanced the physical properties of the asphalt binder, leading to substantial enhancements in asphalt concrete mixture performance against moisture damage. A 6% dosage of NS and NT showed the best performance, with NS performing slightly better than NT. TSR was increased by 14.72 and 11.55 and IRS by 15.60 and 12.75, respectively, with 6% NS and NT compared with mixtures without nanomaterials (0% NM). Full article

This paper presents an innovative scheme to enhance the efficiency of power extraction from wind energy conversion systems (WECSs) under random loads. The study investigates how stochastic load consumption, modeled and predicted using a Markov chain process, impacts WECS efficiency. The suggested approach regulates the rectifier voltage rather than the rotor speed, making it a sensorless and reliable method for small-scale WECSs. Nonlinear WECS dynamics are represented using Takagi–Sugeno (TS) fuzzy modeling. Furthermore, the closed-loop system’s stochastic stability and recursive feasibility are guaranteed regardless of random load changes. The performance of the suggested controller is compared with the traditional perturb-and-observe (P&O) algorithm under varying wind speeds and random load variations. Simulation results show that the proposed approach outperforms the traditional P&O algorithm, demonstrating higher tracking efficiency, rapid convergence to the maximum power point (MPP), reduced steady-state oscillations, and lower error indices. Enhancing WECS efficiency under unpredictable load conditions is the primary contribution, with simulation results indicating that the tracking efficiency increases to 99.93%. Full article

Wind-induced waves can lead to the partial or complete wash-over of beaches, causing erosion that impacts both the landscape and tourist infrastructure. In some regions of the world, e.g., Croatia, this process, which usually occurs during a harsh winter, has a major impact on the environment and the economy, and preventing or reducing this process is highly desirable. One of the simplest methods to reduce or prevent beach erosion is the use of innovative underwater structures designed to decrease wave energy by reducing wave height. In this study, submerged breakwaters are numerically investigated using various topologies, positions, and angles relative to the free surface. Not only is the optimal topology determined, but the most efficient arrangement of multiple breakwaters is also determined. The advantage of newly developed submerged breakwaters over traditional ones (rock-fixed piers) is that they do not require complex construction, massive foundations, or high investment costs. Instead, they comprise simple floating bodies connected to the seabed by mooring lines. This design makes them not only cheap, adaptable, and easy to install but also environmentally friendly, as they have little impact on the seabed and the environment. To evaluate wave damping effectiveness, the incompressible computational fluid dynamics (ICFD) method is used, which enables the use of a turbulence model and the possibility of accurate wave modelling. Full article

Antimicrobial peptides represent a promising alternative to traditional drugs in relation to cost, toxicity, and, primarily, the growing problem of drug resistance. Here, we report on the activity against HSV-1 and HSV-2 of a previously described wide-spectrum synthetic decapeptide, Killer Peptide (KP). As determined by plaque reduction assays, treatment with KP at 100 μg/mL resulted in a reduction in the viral yield titer of 3.5 Logs for HSV-1 and 4.1 Logs for HSV-2. Further evaluation of KP antiviral activity focused on the early stages of the virus replicative cycle, including the determination of the residual infectivity of viral suspensions treated with KP. A direct effect of the peptide on viral particles impairing virus absorption and penetration was shown. The toxicity profile proved to be extremely good, with a selectivity index of 29.6 for HSV-1 and 156 for HSV-2. KP was also active against acyclovir (ACV)-resistant HSV isolates, while HSV subcultures in the presence of sub-inhibitory doses of KP did not lead to the emergence of resistant strains. Finally, the antiviral action of KP proved to be synergistic with that of ACV. Overall, these results demonstrate that KP could represent an interesting addition/alternative to acyclovir for antiviral treatment. Full article

Flood-susceptibility mapping (FSM) is crucial for effective flood prediction and disaster prevention. Traditional methods of modeling flood vulnerability, such as the Analytical Hierarchy Process (AHP), require weights defined by experts, while machine-learning and deep-learning approaches require extensive datasets. Remote sensing is also limited by the availability of images and weather conditions. We propose a new hybrid strategy integrating deep learning with the HEC–HMS and HEC–RAS physical models to overcome these challenges. In this study, we introduce a Weighted Residual U-Net (W-Res-U-Net) model based on the target of the HEC–HMS and RAS physical simulation without disregarding ground truth points by using two loss functions simultaneously. The W-Res-U-Net was trained on eight sub-basins and tested on five others, demonstrating superior performance with a sensitivity of 71.16%, specificity of 91.14%, and area under the curve (AUC) of 92.95% when validated against physical simulations, as well as a sensitivity of 88.89%, specificity of 93.07%, and AUC of 95.87% when validated against ground truth points. Incorporating a “Sigmoid Focal Loss” function and a dual-loss function improved the realism and performance of the model, achieving higher sensitivity, specificity, and AUC than HEC–RAS alone. This hybrid approach significantly enhances the FSM model, especially with limited real-world data. Full article

To address SDG12 (ensure sustainable consumption and production patterns), and to provide technical evidence for alternative concrete constituents to traditional natural river sand, stone fine aggregate (SFA), brick fine aggregate (BFA), ladle-refined furnace slag aggregate (LFS), recycled brick fine aggregate (RBFA), and washed waste fine aggregate (WWF), ready-mix concrete plants were investigated. Concrete and mortar specimens were made with different variables, such as replacement volume of natural sand with different alternative fine aggregates, water-to-cement ratio (W/C), and sand-to-aggregate volume ratio (s/a). The concrete and mortar specimens were tested for workability, compressive strength, tensile strength, and Young’s modulus (for concrete) at 7, 28, and 90 days. The experimental results show that the compressive strength of concrete increases when natural sand is replaced with BFA, SFA, and LFS. The optimum replacement amounts are 30%, 30%, and 20% for BFA, SFA, and LFS, respectively. For RBFA, the compressive strength of concrete is increased even at 100% replacement of natural sand by RBFA. For WWF, the compressive strength of concrete increases up to a replacement of 20%. Utilizing these alternative fine aggregates can be utilized to ensure a circular economy in construction industries and reduce the consumption of around 30% of natural river sand. Full article

Automated packing is urgently needed in apple production. This paper proposes an improved genetic algorithm fused with an optimal parameter selection algorithm to optimize the two-posture manipulator working path of packing robots. First, the structure and working principle of the packing robot were designed. Second, the kinematics and packing paths of the two-posture manipulator were analyzed. Finally, the path optimization method for the two-posture manipulator was introduced. The method was based on the improved genetic algorithm by using a two-level coding and region crossover operator. The parameter values can be automatically determined by the optimal parameter selection algorithm. Ten repeated comparative tests show that the total packing time is 23.86 s under the working conditions of four grasping points and fourteen placing points. The optimal performance of the proposed algorithm is better than that of the traditional genetic algorithm, and the average optimization amplitudes are 14.63%, 15.42%, 16.24%, and 13.82% for 9-groove, 12-groove, 14-groove, and 16-groove trays, respectively. The proposed algorithm can effectively prevent the premature convergence problem of the traditional genetic algorithm and the optimization process instability problem, improve the range of optimization, and reduce the manipulator working time during packing. Full article

Multi-objective optimization algorithms are crucial for addressing real-world problems, particularly with regard to optimizing well control parameters, which are often computationally expensive due to their reliance on numerical simulations. Surrogate-assisted models help to reduce this computational burden, but their effectiveness depends on the quality of the surrogates, which can be affected by candidate dimension and noise. This study proposes a novel surrogate-assisted multi-objective optimization framework (MOO-SESA) that combines selective ensemble support-vector regression with NSGA-II. The framework’s uniqueness lies in its adaptive selection of a diverse subset of surrogates, established prior to iteration, to enhance accuracy, robustness, and computational efficiency. To our knowledge, this is the first instance in which selective ensemble techniques with multi-objective optimization have been applied to reservoir well control problems. Through employing an ensemble strategy for improving the quality of the surrogate model, MOO-SESA demonstrated superior well control scenarios and faster convergence compared to traditional surrogate-assisted models when applied to the SPE10 and Egg reservoir models. Full article

In 2015, the Japanese public broadcaster NHK aired an educational series that re-examined traditional fairy tales by putting their characters on trial for their immoral behavior, such as revenge, violence, and dishonesty. These tales, rooted in premodern Japanese folklore, were widely available in various book formats by the late nineteenth century and, unlike modern adaptations, they did not sanitize violence or evil. This study analyzes four miniature picture books from the late nineteenth century that recount the story, Kachikachi yama (The Crackling Mountain). This analysis focuses on both verbal and visual representations of good and evil, with attention to themes of loyalty, filial piety, and virtuous revenge. The findings reveal that these picture books presented young readers with complex moral lessons, where the boundaries between good and evil were blurred. Additionally, they illuminate the prevailing image of children during that era, depicting them as “little adults” expected to be educated and prepared for the practical realities of the adult world. Full article

Phylogeographic studies have been conducted on many different mammal species in order to track their recent demographic histories. The climatic fluctuations associated with the Last Glacial Maximum (LGM) appear to have had a profound influence on the geographic patterning of genetic diversity in mammals. However, most phylogenetic studies have focused on single species. Few have used a holistic approach covering multiple taxa to explore common patterns. Here, we conducted meta-analyses of mitochondrial DNA control region sequences, identifying haplotype diversity and private allelic richness patterns in a geographic context. Four general patterns emerged among European mammals: an east–west decline in variation, a Western-Central belt of the highest diversity, southern richness, and homogeneity with no geographic pattern. These patterns likely reflect the refugial origins of modern populations. The east–west variation decline suggests species with eastern LGM refugia; the Western-Central belt of the highest diversity may harbor taxa with cryptic northern refugia, while southern richness may correspond to traditional southern refugia. Species with homogeneity and no geographic pattern may have been panmictic without a specific refugium or may reflect the occurrence of both southern and cryptic northern refugia. Surprisingly, the “no pattern” phenomenon is seldom discussed and may frequently have been discounted. Our study emphasizes the importance of considering multiple taxa, providing valuable insights into the responses of European mammals to past climatic changes. Full article

Modern advances in technology have increased the demand for traditional accounting systems to be upgraded for real-time data processing, security, and standardized reports. Thus, this paper proposes a new accounting information system that integrates IoT, blockchain, and XBRL. The proposed system aims to automate the accounting process by using IoT to collect data and send it automatically to a blockchain, which acts as a database that will generate journal entries automatically through smart contracts. XBRL will then be used as an output method for standardized financial reports based on the data transferred from the blockchain. This paper uses a qualitative research design based on semi-structured interviews with 13 industry experts from IT engineering, academia, and financial systems analysis. NVivo software was used to conduct a thematic analysis of interview transcripts. The findings demonstrated that integrating IoT, blockchain, and XBRL is technically feasible, with significant potential to enhance accounting systems. Additionally, the findings identified key challenges of the proposed system, including the complexity of integration, data validation across technologies, costs, user adoption, and scalability concerns. However, the results showed that this system offers substantial benefits, such as real-time data capture from IoT devices, secure data storage and immutability through blockchain, standardized financial reporting via XBRL, accounting process automation, improved data accuracy, and enhanced security and transparency in financial reporting. The study also identified an optimal mechanism for ensuring seamless data transmission between these technologies. The study makes a valuable contribution to the accounting field by providing a new framework for automating data collection, enhancing data security, and streamlining financial reporting, with significant potential to advance accounting systems and improve transparency, accuracy, and efficiency in financial reporting. The study’s potential to impact accounting systems and financial reporting research and practice emphasizes its importance. Full article

This paper introduces a novel Particle Swarm Optimization (RLPSO) algorithm based on reinforcement learning, embodying a fundamental symmetry between global and local search processes. This symmetry aims at addressing the trade-off issue between convergence speed and diversity in traditional algorithms. Traditional Particle Swarm Optimization (PSO) algorithms often struggle to maintain good convergence speed and particle diversity when solving multi-modal function problems. To tackle this challenge, we propose a new algorithm that incorporates the principles of reinforcement learning, enabling particles to intelligently learn and adjust their behavior for better convergence speed and richer exploration of the search space. This algorithm guides particle learning behavior through online updating of a Q-table, allowing particles to selectively learn effective information from other particles and dynamically adjust their strategies during the learning process, thus finding a better balance between convergence speed and diversity. The results demonstrate the superior performance of this algorithm on 16 benchmark functions of the CEC2005 test suite compared to three other algorithms. The RLPSO algorithm can find all global optimum solutions within a certain error range on all 16 benchmark functions, exhibiting outstanding performance and better robustness. Additionally, the algorithm’s performance was tested on 13 benchmark functions from CEC2017, where it outperformed six other algorithms by achieving the minimum value on 11 benchmark functions. Overall, the RLPSO algorithm shows significant improvements and advantages over traditional PSO algorithms in aspects such as local search strategy, parameter adaptive adjustment, convergence speed, and multi-modal problem handling, resulting in better performance and robustness in solving optimization problems. This study provides new insights and methods for the further development of Particle Swarm Optimization algorithms. Full article

Incorporating waste to produce new environmentally friendly construction products has become one of the great challenges of the industry nowadays. The aim of this research is to analyse the behaviour of novel gypsum composites against water action, incorporating recycled rubber aggregates (up to 8.5% vol.) and dissolved expanded polystyrene (up to 10.0% vol.). To this end, a total of 10 dosages have been proposed with the progressive substitution of natural resources by these secondary raw materials. The results show how it is possible to reduce the total water absorption of the gypsum composites by up to 8.3% compared to traditional gypsum material. In addition, it is also possible to reduce water absorption by capillary by up to 52.7%, resulting in lighter composites with good performance against water action. In all composites analysed, the mechanical strengths exceeded the minimum values of 1 MPa in bending and 2 MPa in compression, making them an optimal solution for the development of lightweight prefabricated products for damp rooms. Full article

The nutcracker optimizer algorithm (NOA) is a metaheuristic method proposed in recent years. This algorithm simulates the behavior of nutcrackers searching and storing food in nature to solve the optimization problem. However, the traditional NOA struggles to balance global exploration and local exploitation effectively, making it prone to getting trapped in local optima when solving complex problems. To address these shortcomings, this study proposes a reinforcement learning-based bi-population nutcracker optimizer algorithm called RLNOA. In the RLNOA, a bi-population mechanism is introduced to better balance global and local optimization capabilities. At the beginning of each iteration, the raw population is divided into an exploration sub-population and an exploitation sub-population based on the fitness value of each individual. The exploration sub-population is composed of individuals with poor fitness values. An improved foraging strategy based on random opposition-based learning is designed as the update method for the exploration sub-population to enhance diversity. Meanwhile, Q-learning serves as an adaptive selector for exploitation strategies, enabling optimal adjustment of the exploitation sub-population’s behavior across various problems. The performance of the RLNOA is evaluated using the CEC-2014, CEC-2017, and CEC-2020 benchmark function sets, and it is compared against nine state-of-the-art metaheuristic algorithms. Experimental results demonstrate the superior performance of the proposed algorithm. Full article