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Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling
No full textJob shop scheduling is a process of optimising the use of limited resources to improve the production efficiency. Job shop scheduling has a wide range of applications such as order picking in the warehouse and vaccine delivery scheduling under a pandemic. In real-world applications, the production environment is often complex due to dynamic events such as job arrivals over time and machine breakdown. Scheduling heuristics, e.g., dispatching rules, have been popularly used to prioritise the candidates such as machines in manufacturing to make good schedules efficiently. Genetic programming, has shown its superiority in learning scheduling heuristics for job shop scheduling automatically due to its flexible representation. This survey firstly provides comprehensive discussions of recent designs of genetic programming algorithms on different types of job shop scheduling. In addition, we notice that in the recent years, a range of machine learning techniques such as feature selection and multitask learning, have been adapted to improve the effectiveness and efficiency of scheduling heuristic design with genetic programming. However, there is no survey to discuss the strengths and weaknesses of these recent approaches. To fill this gap, this paper provides a comprehensive survey on genetic programming and machine learning techniques on automatic scheduling heuristic design for job shop scheduling. In addition, current issues and challenges are discussed to identify promising areas for automatic scheduling heuristic design in the future
An ML-based wind turbine blade design method considering multi-objective aerodynamic similarity and its experimental validation
No full textModel test is an essential technique to study the aerodynamic performance of wind turbines. To overcome the poor aerodynamic performance of scaled models caused by the scaling effect, this study proposes an innovative blade design method for scaled model testing based on machine learning (ML). The method achieves satisfactory similarity between the thrust and power coefficients under multiple operating conditions of the model and prototype. Furthermore, a case study of the NREL 5-MW wind turbine is carried out with wind tunnel tests to validate the effectiveness of the proposed method. Obtained results suggest that the aerodynamic performance of redesigned blade closely mirrors that of the prototype under multiple operating conditions, reaching 97.59 % (thrust) and 97.87 % (power) coefficients of the prototype at the rated operating condition, respectively. With this technique, aerodynamic performance similarities between the redesigned blade and the prototype can be enhanced, contributing to more accurate scale model testing
Anomaly Detection for Space Information Networks: A Survey of Challenges, Techniques, and Future Directions
No full textSpace anomaly detection plays a critical role in safeguarding the integrity and reliability of space systems amid the rising tide of threats. This survey aims to deepen comprehension of space cyber threats through space threat modeling, and meticulously examine the unique challenges of space anomaly detection. The survey identifies scalability, real-time detection, limited labeled data availability, concept drift, and adversarial attacks as key challenges based on thorough literature analysis and synthesis. By extensively exploring state-of-the-art anomaly detection techniques, the study evaluates their applicability, strengths, and limitations within space networks. Going beyond analysis, a notable contribution of this work involves integrating stream-based and graph-based methods, tailored to capture the intricate temporal and structural relationships inherent in space networks. This innovative hybrid approach holds promise for heightened detection accuracy and sets the stage for future research endeavors. As space threats continue evolving in both number and sophistication, this survey timely provides insights, recommendations, and a clear roadmap for researchers, engineers, and practitioners to fortify space anomaly detection mechanisms
The links between parental smoking and childhood obesity: data of the longitudinal study of Australian children
No full textChildhood obesity is one of the most concerning public health issues globally and its implications in mortality and morbidity in adulthood are increasingly important. This study uses a unique dataset of Australian children aged 4–16 to examine the impact of parental smoking on childhood obesity. It confirms a significant link between parental smoking (stronger for mothers) and higher obesity risk in children, regardless of income, age, family size, or birth order. Importantly, we explore whether heightened preference for unhealthy foods can mediate the effect of parental smoking. Our findings suggest that increased consumption of unhealthy foods among children can be associated with parental smoking
Vertical accuracy assessment of freely available global DEMs (FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM) in flood-prone environments
No full textFlood models rely on accurate topographic data representing the bare earth ground surface. In many parts of the world, the only topographic data available are the free, satellite-derived global Digital Elevation Models (DEMs). However, these have well-known inaccuracies due to limitations of the sensors used to generate them (such as a failure to fully penetrate vegetation canopies and buildings). We assess five contemporary, 1 arc-second (≈30 m) DEMs -- FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM -- using a diverse reference dataset comprised of 65 airborne-LiDAR surveys, selected to represent biophysical variations in flood-prone areas globally. While vertical accuracy is nuanced, contingent on the specific metrics used and the biophysical character of the site being assessed, we found that the recently-released FABDEM consistently ranked first, improving on the second-place Copernicus DEM by reducing large positive errors associated with forests and buildings. Our results suggest that land cover is the main factor explaining vertical errors (especially forests), steep slopes are associated with wider error spreads (although DEMs resampled from higher-resolution products are less sensitive), and variable error dependency on terrain aspect is likely a function of horizontal geolocation errors (especially problematic for AW3D30 and Copernicus DEM)
Opto-electro-thermo-mechanical behaviours of perovskite plates
No full textMetal-halide perovskites are rapidly emerging as promising candidates for next-generation solar cells and photoelectric technologies. However, their structural behaviours considering opto-electro-thermo-mechanical properties in multi-physics fields have not yet been fully understood, thus limiting their engineering applications. This paper proposes a new opto-electro-thermo-elastic model taking into account photostriction, photothermal effect, electrostriction, and piezoelectricity for lead halide perovskites and investigates their static and dynamic opto-electro-thermo-mechanical behaviours. The governing equations of perovskite plates are derived within the theoretical framework of first-order shear deformation theory and are numerically solved using the Chebyshev-Ritz method. Comprehensive parametric studies are carried out to examine the influences of multi-physics fields on the critical light intensity, critical electric field, static bending, elastic buckling, free vibration, as well as dynamic responses of perovskite plates. Numerical results show that light illumination, photoinduced temperature rise, and applied electric field play an important role in bending deformation, dynamic deflection, critical buckling load, and free vibration frequency hence need to be taken into consideration for the design of perovskite-based photoelectric devices
Investigation into the physiochemical properties of soy protein isolate and concentrate powders from different manufacturers
No full textThe physiochemical properties of five commercially available soy protein isolates (SPI) from different manufacturers and one soy protein concentrate were analysed. Despite their identical botanical origin and an almost interchangeable molecular weight profile, remarkable differences were revealed in their solubility, protein dispersibility index (PDI), water holding capacity (WHC), zeta potential (measured at different pHs) and particle size distribution. Protein solubility and PDI values, which can be considered as a simple and reliable measure of soy protein powder's suitability for industrial formulations such as extrusion premixes, revealed to be strongly intercorrelated, with SPI A, B and E showing higher values at pH 7.0 and 9.0 as compared to their counterparts (sample C and D). WHC appeared to be less influenced by solubility, but greatly by particle size distribution. SPI A, B and E showed largest increase in diameter upon hydration (‘swelling’) and gave the highest WHC
Femtosecond laser machining of the novel superhydrophobic microstructure for the oil-water separation
No full textBionic micro/nano structures with superhydrophobicity is extensively used in oil-water separation. However, the fabrication of such tiny structures involves intricate processes, often requiring additional steps such as chemical or mechanical post-processing. With the development of the laser machining, it is feasible to fabricate structures in micro/nano scale via the ablation of ultrafast laser beam. Inspired by the function of the funnel, a novel through-hole structure was designed and manufactured by femtosecond laser in high dimensional precision. The copper film with the funnel-like through-hole structure exhibited remarkable hydrophobic properties, with a water contact angle of 160 ± 2° and a sliding angle ≤ 5°, facilitating the high-quality and high-efficiency separation of water-oil/organic liquids, compared with the conventional through-hole structures, such as circular or rectangular structures. Furthermore, the prepared film demonstrated exceptional capabilities of anti-freezing, anti-pollution, environmental stability, and versatility, examined by enduring treatments involving acidic liquids, immersion in hot solutions, freezing, and scratching. In sum, this work offers innovative concepts and valuable insights on the design and fabrication of functional structures with ultrafast laser machining
A new semi-empirical correlation for estimating settling dynamics of suspensions in viscoelastic shear-thinning fluids
No full textThe optimization of sedimentation and hydrotransport systems involving settling suspensions in viscoelastic fluids has been hindered by a lack of robust and scalable mathematical models that accurately characterize settling dynamics in such scenarios. This study aimed to enhance understanding of this problem by developing a semi-empirical correlation that is dependent on space and time. This correlation describes batch settling characteristics during the ‘acceleration phase’ of settling, including changes in settling velocity and solids concentration of suspensions in a viscoelastic shear-thinning fluid under quiescent conditions. A novel dimensionless ratio- the ‘particle-acceleration’ number- was developed to simulate time-dependent settling velocities. The correlation's accuracy was confirmed through validation which involved predicting two key parameters- the duration of the acceleration settling phase (with 86–98% accuracy) and the final solids concentration at the sample depth once the acceleration phase ended (with 82–96% accuracy)
Key trends in the response of suction bucket foundations to extreme axial cyclic loads
No full textThe offshore wind industry is currently expanding into emerging markets at a rapid pace. Some of these markets are located in areas characterized by frequent extreme events. From a geotechnical perspective, this results in new design challenges, as foundations must withstand severe loads repeatedly throughout their intended lifetimes. Jacket structures resting on suction buckets represent a new foundation concept that still requires research to become a potential optimal solution. The vertical cyclic response of bucket foundations constitutes the main topic of this article. The current study is based on observations of the behavior of a scaled model installed in dense sand. Both normal and extreme conditions were simulated by applying axial cyclic loads of varying amplitudes, means, and frequencies. High amplitude and low frequency cause significant stiffness degradation and permanent displacement. These scenarios occur due to build-up of excess pore pressure, with subsequent triggering of liquefaction. A criterion for liquefaction occurrence is identified and may be readily used for practical applications. Considerable levels of tensile loading lead to a high rate of heave, regardless of frequency. For one-way compressive forces, after an extreme loading sequence, stiffness returns to its initial level, as long as no liquefaction develops priorly. This bears essential implications in predicting the change of natural frequency of the system