Technological development within Industry 4.0 will lead to reduced risks of working conditions. A strategic change in the incidence of threats in the context of Industry 4.0 will place significant emphasis on changing people’s education and training. One of the strategic tasks in the near future is adequate education, currently referred to as Education 4.0. Meeting the challenges of Industry 4.0 will require the acquisition of digital literacy. To define the current state of readiness of companies for cooperation, research was carried out in the form of a questionnaire experiment. Companies that have Industry 4.0 technologies in place participated in the research. The results of the experiment are recommendations for formulating the content of educational processes in all their forms. It has been confirmed that part of Strategy Industry 4.0 is and will always be human. Therefore, it is important to create conditions for its safety both in the workplace and in everyday life. One of the results of the research was the confirmation that the Covid-19 pandemic accelerated the development and application of the implementation of Strategy Industry 4.0 into industrial practice.
The shear strength reduction method (SSRM) is a standard method in slope stability enabling to determine the factor of safety and related failure zones. In case of the non-associated Mohr-Coulomb model, the method can oscillate with respect to the refinement of a finite element mesh. To suppress this drawback, the non-associated model is approximated by the associated one such that the strength parameters are reduced by using a function depending on a scalar factor and on the effective friction and dilatancy angles. This modification (MSSRM) can be easily implemented in commercial codes like Plaxis or Comsol Multiphysics. Next, an optimization approach to the modified SSRM (OPT-MSSRM) is introduced. It is shown that the optimization problem is well-defined and can be analyzed by variational principles. For its solution, a regularization method is combined with mesh adaptivity and implemented in Matlab. The SSRM, MSSRM and OPT-MSSRM methods are compared on numerical examples representing a case study of a real heterogeneous slope.
Thermochemical conversion of biomass has been considered a promising technique to produce alternative renewable fuel sources for future energy supply. However, these processes are often complex, labor-intensive, and time-consuming. Significant efforts have been made in developing strategies for modeling thermochem-ical conversion processes to maximize their performance and productivity. Among these strategies, machine learning (ML) has attracted substantial interest in recent years in thermochemical conversion process optimization , yield prediction, real-time monitoring, and process control. This study presents a comprehensive review of the research and development in state-of-the-art ML applications in pyrolysis, torrefaction, hydrothermal treatment, gasification, and combustion. Artificial neural networks have been widely employed due to their ability to learn extremely non-linear input-output correlations. Furthermore, the hybrid ML models out-performed the traditional ML models in modeling and optimization tasks. The comparison between various ML methods for different applications, and insights about where the current research is heading, is highlighted. Finally, based on the critical analysis, existing research knowledge gaps are identified, and future recommendations are presented.
Advancement in the anaerobic digestion of sewage sludge for biogas production is constantly required for effective commercialization. There is great interest in exploring the scientometric and technical assessment in biogas production using sewage sludge from various sources in the anaerobic digestion process. Therefore, this work provides a bibliometric and technical study of anaerobic digestion for biogas production from sewage sludge in the last ten years, based on Web of Science data. The vital parameters of the bibliometric study are authors, countries, and institutional collaboration along with the authors' keywords. In terms of technical assessment, challenges are highlighted with critical analysis of previous work performed both on a laboratory scale and on a pilot scale. The study recommends key considerations and new combination techniques to enhance biohydrogen production with optimized process conditions which will provide vital economic impact for technological adaptation and commercialization.
The purpose of this article is to present a perspective, environmentally friendly, and cost-effective chemical approach for preparing photocatalysts that can be used in wastewater treatment. One nanometer-sized Ag nanoparticles (NPs) were deposited on the surface of a commercial titanium dioxide mixture of anatase and rutile. A novel low-temperature chemical method using an ultrasonication process without a reducing agent was applied to surface-modified TiO2 grains with Ag NPs. The materials were characterized by several complementary techniques, such as X-ray powder diffraction (XRD), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), UV–Vis diffuse reflectance (DRS), Raman spectroscopy, X-ray electron spectroscopy (XPS), Mott-Schottky analysis, and N2 adsorption-desorption measurements. The silver content in the prepared samples ranged from 0.1 to 0.5 wt% (low Ag content) and from 1 to 5 wt% (high Ag content). The photocatalytic performance of the pristine and Ag NPs modified TiO2 powders in photodegradation of the acid orange 7 model dye was studied under ultraviolet (UV), visible (Vis) and UV + Vis radiation. This study explores, for the first time, a photodegradation mechanism for all combinations of wavelengths and silver contents, which is of practical importance in selecting the best photocatalyst and irradiation conditions.
High-throughput (HT) computations and machine learning (ML) algorithms are two fundamental approaches in data-driven paradigms to predict various properties of solids due to their efficiency in data creation and model construction, which however are usually used individually and lack generalization and flexibility. In this paper, we propose a scheme combining HT computations for the efficient creation of consistent data and ML algorithms for the fast construction of surrogate models to screen B-N solids' stability and mechanical properties at ambient and high pressures. Employing HT computations, a standardized database of formation enthalpy, elasticity and ideal strength of thousands of B-N structures with high precision is first established. Then several ML models are comparatively built employing the XGBoost approach with the consideration of four descriptors, i.e., sine matrix, Ewald sum matrix, SOAP, and MBTR. Our results suggest the MBTR provides more accurate estimates of various physical properties except for bulk modulus, which is evaluated with greater precision by the Ewald sum matrix. To further improve the model interpretability, based on the brittleness/ductility criterion of materials, a symbolic model with strong physical significance is successfully built for the ideal strength of the B-N solids through the key descriptors screened by the ML methods, showing great accuracy. Our research demonstrates the possibility of building high-efficient ML models and compact symbolic physical models by incorporating consistent data through HT computations for high accuracy in predicting the thermodynamic and mechanical properties of strong solids with high precision, providing a pathway for inverse design of novel materials.
The Co3O4 modified with 1 wt.% Cs was deposited on α-Al2O3 open-cell foam covered with different washcoats (MgO, MnO2, SiO2, TiO2), investigated by XRD, Raman microspectroscopy, nitrogen physisorption, XPS, SEM and TPR-H2 in order to elucidate interactions between Cs-Co3O4 and the washcoat and their effect on surface area, reducibility, dispersion, and low temperature decomposition of N2O. The samples with SiO2 and TiO2 washcoats had the largest surface areas. Only SiO2 did not interact with the active phase and did not change the reducibility of the catalyst. Although the same Cs concentration was adjusted during preparation of all catalysts, differences in the Cs/Co surface molar ratio were observed due to a different level of Co3O4 particles aggregation and cesium dispersion. Catalytic activity correlated with the surface Cs/Co molar ratio closely interconnected with the surface Co³⁺/Co²⁺ molar ratio while there were no direct relationships to the redox properties and surface area. The highest activity was achieved for the MgO washcoat prepared from carbonate with the highest Co³⁺/Co²⁺ molar ratio corresponding to the optimal Cs/Co surface molar ratio around 0.1.
The occurrence of chemical compounds specific to individual plastic polymers allowed the determination of components in compost feedstock (urban greenery and bio bins of households). The total concentration of plastic polymers is 431 mg/kg. The main identified polymers are polystyrene, polyethylene terephthalate, polyethylene, polycarbonate, and polypropylene. Additives used to modify the properties of plastics are present in the concentration of 195 mg/kg. Twelve compounds used as additives in the production of plastics in both the feedstock and the compost were identified. A statistically significant relationship was found between the decrease of concentration of organic matter and concentrations of compounds that identify plastic polymers (polyethylene terephthalate and polystyrene). The decrease of concentrations of compounds specific to polymers after three months was observed in the range between 33 and 84%, while the decrease for additives was 68%. After 18 months, the loss was higher than 82% except for polypropylene (only 70%). Compound leachability after three months of composting decreased to one-quarter for most polymers (except polypropylene and polyethylene), and to one-third for additives.
In this article, a novel, pilot-scale gasification technology is closely described from the technological and design points of view. The construction of the fuel bed within the reactor is circular, operating according to the sliding bed principle, equipped with a tangential oxidiser intake. The technology combines principles of cross-draft and updraft gasification reactor type in an autothermal regime. In the model process with softwood pellets (spruce wood) as source fuel, the LHV of the producer gas reached 4.3 MJ·m⁻³, with the overall conversion ratio reaching 80.3%. These results were obtained in a 709 ± 10 °C environment with the fuel feed rate equal to exactly 30 kg·h⁻¹ while the flow rate of the oxidising media was 17 ± 1 m³∙h⁻¹. The gas quality in terms of its content is a major factor to be considered. The purity of the producer gas is crucial for most final-use technologies. Thus, the question of polluting agents and undesired substances is analysed and discussed in this article. The custom-made cleaning track of hereby described scientific technology can operate with 99.9% particulate matter removal efficiency, while tar compounds within the producer gas are kept as low as 9.7 g·m⁻³. This article summarises a detailed description of a specific pilot-scale gasification unit where results of an experimental analysis are depicted along with real-time values and detailed schematic descriptions and illustrations, providing a base for comparison with conventional technology designs.
Access to employment through the Internet matters a great deal to stabilise the livelihood of migrant peasant workers in Chinese cities. This study examines how Internet usage affects the off-farm income of migrant peasant workers by constructing a random effects model for the period 2010–2016. Research findings corroborate that Internet usage has significantly increased the off-farm income of migrant peasant workers and the positive impact of Internet usage on income is stronger for migrant peasant workers than for their urban flexible-employed counterparts. The positive impacts of Internet usage on migrant peasant workers’ income vary regarding region, gender, and educational level. It is concluded that Internet usage has helped improve the livelihood resilience of migrant peasant workers in China.
Recently, the supercapacitor has gained more consideration due to its speedy charging and discharging, high power density, and stability compared to the existing batteries. Activated carbon-based electrodes for the supercapacitor provide higher specific capacitance. In this research, activated carbon was obtained from Prunus dulcis (almond fruit) shell by carbonization using a muffle furnace. Carbonized Prunus dulcis fruit shells were chemically activated by potassium hydroxide (KOH). X-ray diffraction (XRD) patterns of KOH-activated carbon derived from Prunus dulcis shell evident that the activated carbon samples are amorphous. The scanning electron microscope (SEM) images of activated carbon derived from Prunus dulcis exhibited a 2D sheet-like morphology and a smooth surface. Energy-dispersive X-ray spectroscopy (EDX) detected oxygen, chloride, and potassium peaks with 85.2% carbon. The addition of KOH helped to increase the porosity of the fruit shells and enhanced the absorption of the electrolyte. The supercapacitor electrode was prepared by coating activated carbon on a graphite pencil lead. The performance of the electrode was evaluated using a 6 M KOH electrolyte at various current densities and scan rates. The prepared sample was electrochemically characterized by cyclic voltammetry, galvanostatic charge and discharge measurements, and electrochemical impedance spectroscopy. From the analysis, the suitability of the material as an electrode can be understood. The specific capacitance of the samples was measured as 434, 237, 105.9, and 50.5 F g⁻¹ at 1, 2, 4, and 10 A g⁻¹, respectively. The higher specific capacitance is ascribed to the high specific surface area, electrolyte, and pore volume. And also, at an energy density of 0.28 Wh g⁻¹, the power density of 100 kW g⁻¹ is obtained. The electrode has a series resistance of 10.51 Ω and a charge transfer resistance of 1.12 Ω.
This paper introduces a novel algorithm for effective and accurate extraction of non-invasive fetal electrocardiogram (NI-fECG). In NI-fECG based monitoring, the useful signal is measured along with other signals generated by the pregnant women’s body, especially maternal electrocardiogram (mECG). These signals are more distinct in magnitude and overlap in time and frequency domains, making the fECG extraction extremely challenging. The proposed extraction method combines the Grey wolf algorithm (GWO) with sequential analysis (SA). This innovative combination, forming the GWO-SA method, optimises the parameters required to create a template that matches the mECG, which leads to an accurate elimination of the said signal from the input composite signal. The extraction system was tested on two databases consisting of real signals, namely, Labour and Pregnancy. The databases used to test the algorithms are available on a server at the generalist repositories (figshare) integrated with Matonia et al. (Sci Data 7(1):1–14, 2020). The results show that the proposed method extracts the fetal ECG signal with an outstanding efficacy. The efficacy of the results was evaluated based on accurate detection of the fQRS complexes. The parameters used to evaluate are as follows: accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1 score. Due to the stochastic nature of the GWO algorithm, ten individual runs were performed for each record in the two databases to assure stability as well as repeatability. Using these parameters, for the Labour dataset, we achieved an average ACC of 94.60%, F1 of 96.82%, SE of 97.49%, and PPV of 98.96%. For the Pregnancy database, we achieved an average ACC of 95.66%, F1 of 97.44%, SE of 98.07%, and PPV of 97.44%. The obtained results show that the fHR related parameters were determined accurately for most of the records, outperforming the other state-of-the-art approaches. The poorer quality of certain signals have caused deviation from the estimated fHR for certain records in the databases. The proposed algorithm is compared with certain well established algorithms, and has proven to be accurate in its fECG extractions.
This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.
Wire electrical discharge machining (WEDM) is an unconventional machining technology that is indispensable in many industries. Machining is performed using the thermoelectric principle, while it is possible to machine all at least minimally electrically conductive materials. Due to the wide range of applications of WEDM, it is necessary to ensure the appropriate quality of machined surfaces, regardless of the thickness of the machined materials, while maintaining an acceptable cutting speed. For this purpose, this study was performed to analyse the effect of material thickness on the cutting speed, morphology and topography of Ampcoloy 35 material. In this study, thicknesses from 5 to 160 mm were analysed in 5 mm increments with the same machine parameter settings. The surface topography and morphology were studied using electron and light microscopy, and cross-sections of the sample were created to examine the condition of the subsurface layer. It was found that the values of Ra at the edge ranged from 1.8 to 3.2 µm and in the middle of the sample from 1.7 to 3 µm, while the trend of increasing Ra with thickness is not visible. It is clear from the morphology analysis that a rugged surface with more craters was created at the edges than in the middle of the sample, while segregated lead needles were also formed at the edges of the samples.
Geophysics on the Moon can probe diverse present conditions of the Moon to understand constraining parts of the Moon’s history that was primarily driven by a broad spectrum of impact craters, each of them of contrasting age, size, and complexity. Geophysical methods probe for crustal thickness, water presence, crustal density properties (via the gravity aspects), temperature, composition, and magnetic state, all deciphering the various events that modified the Moon’s surface. This field of knowledge was enhanced by samples returned from the Apollo mission, allowing the combination of geophysical sensing with the limited “ground truth”. Especially rock magnetic and paleomagnetic methods revealed more information about the magnetic field of the Moon.
The combination of gravity aspects with magnetic field intensities and LOLA topography provides a unique description of structural features on the Moon (impact craters, mascons, maria (seas), catenae, ghost craters, polygonal structures, rifts, radial structures, intrusions, or crustal fractures). For this section, we selected unique features from Chap. 8 to show more details.
The gravity field model is a set of harmonic potential coefficients (Stokes parameters). All the gravity aspects are computed from them. We briefly review landmark solutions for the global static gravity field models of the Moon (Table); then we focus on the model we actually use—GRGM1200A—to the degree and order 600 in spherical harmonic expansion (with the ground resolution of ~10 km).
We show maps with the gravity aspects, topography from LOLA, and magnetic intensities. First, we present global views on the Moon (Sect. 8.1), and then closer views by segments of the lunar surface with more details.
A short outline from maps of the Moon, obtained from telescopic observations, to present-day digital high-resolution global (near side and far side, polar areas including) laser altimetry from LOLA (i.e., the data we actually make use of in this Atlas).
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