This paper introduces and studies a new discrete distribution with one parameter that expands the Poisson model, discrete weighted Poisson Lerch transcendental (DWPLT) distribution. Its mathematical and statistical structure showed that some of the basic characteristics and features of the DWPLT model include probability mass function, the hazard rate function for single and double components, moments with auxiliary statistical measures (expectation, variance, index of dispersion, skewness, kurtosis, negative moments), conditional expectation, Lorenz function, and order statistics, which were derived as closed forms. DWPLT distribution can be used as a flexible statistical approach to analyze and discuss real asymmetric leptokurtic data. Moreover, it could be applied to a hyperdispersive data model. Two different estimation methods were derived, i.e., maximal likelihood and the moments technique for the DWPLT parameter, and some advanced numerical methods were utilized for the estimati... [more]

One of China’s ambitious hydrogen strategies over the past few years has been to promote fuel cells. A number of hydrogen refueling stations (HRSs) are currently being built in China to refuel hydrogen-powered automobiles. In this context, it is crucial to assess the dangers of hydrogen leaking in HRSs. The present work simulated the liquid hydrogen (LH2) leakage with the goal of undertaking an extensive consequence evaluation of the LH2 leakage on an LH2 refueling station (LHRS). Furthermore, the utilization of an air curtain to prevent the diffusion of the LH2 leakage is proposed and the defending effect is studied accordingly. The results reveal that the Richardson number effectively explained the variation of plume morphology. Furthermore, different facilities have great influence on the gas cloud diffusion trajectory with the consideration of different leakage directions. The air curtain shows satisfactory prevention of the diffusion of the hydrogen plume. Studies show that with t... [more]

Reliable and user-friendly discrimination of coffee bean integrity and quantification of adulteration in the coffee bean processing value chain would be vital for ensuring consumer trust in quality control and traceability management. In this research, a portable short-wave NIR spectroscopy coupled with chemometric data analysis was employed under different pre-treatments to develop a rapid detection technique. Different pre-processing treatments (multiplicative scatter correction; MSC, standard normal variant; SNV, first derivative; FD) together with multivariate techniques; support vector machine (SVM), linear discriminant analysis (LDA), neural network (NN), and random forest (RF) were comparatively assessed using accuracy and correlation coefficient (R) for discrimination and quantification. The results showed that the FD-LDA model had 97.78% and 100 % in both the calibration set and prediction set. In comparison, the SPA-PLS model had R = 0.9711 and 0.9897 in both the calibration... [more]

In the manufacturing and production of hypoid gears, it is a necessary key problem to improve the tooth surface heat treatment precision and production efficiency of the hypoid gears. How to use advanced statistical theory and methods to evaluate the whole batch machining quality of the tooth surface after heat treatment is particularly urgent. In this connection, for the same batch of hypoid gears with the same gear material, numerical control gear milling method, and heat treatment specifications, a bootstrap-based statistics scheme of tooth surface errors after heat treatment is proposed in this paper. The bootstrap statistics model of the tooth surface errors for the batch hypoid gears is established. The bootstrap probability eigenvalues and confidence intervals of the measurement sequence points on the tooth surface errors are solved, and the optimizing selection of the single sampling numbers and the repeated sampling times is completed. On this basis, by applying the cubic NURB... [more]

Biomass can be a viable supplement and alternative to non-renewable sources of fuel and chemicals. Lignin is an important part of biomass sources which can be used in various chemical and fuel industries. This study explores the pretreatment of lignin from Napier grass using thermal and physical means, as well as extraction of lignin via cellulolytic enzymatic hydrolysis to determine the optimum condition for feedstock pretreatment. Napier grass parts under various drying conditions and particle sizes were treated with enzymes. Moisture analysis, FTIR spectroscopy, UV−Vis analysis, and Klason lignin were carried out to analyze the moisture, functional group, and yield of lignin. Moisture content of the samples were inversely proportional to the drying conditions. The FTIR result showed lower peak intensity for higher drying conditions, while ball-milling showed less reduction in peak intensity. More Klason lignin was extracted under higher drying conditions. The yield of cellulolytic e... [more]

Submarine pipeline gas releases and dispersions can cause safety concerns such as fire and explosion, which can cause serious casualties and property losses. There are many existing studies on the impacts of the horizontal diffusion distances of natural gas leakages from subsea pipelines, but there is a lack of research on the impact of influencing factors on vertical diffusion distances. Therefore, a diffusion model of natural gas leakage from a submarine pipeline is established by using the computational fluid dynamics method (CFD). The influence law and degrees of factors such as water depth at the leakage point, leak orifice size, leak pressure and the ocean current’s velocity on the leakages and vertical diffusion distances of natural gases from submarine pipelines are systematically investigated. The results show that the leaked natural gas jet enters the sea water to form an air mass, which rises continuously under the action of the pressure in the pipe and the buoyancy of the s... [more]

Internet of Health Things plays a vital role in day-to-day life by providing electronic healthcare services and has the capacity to increase the quality of patient care. Internet of Health Things (IoHT) devices and applications have been growing rapidly in recent years, becoming extensively vulnerable to cyber-attacks since the devices are small and heterogeneous. In addition, it is doubly significant when IoHT involves devices used in healthcare domain. Consequently, it is essential to develop a resilient cyber-attack detection system in the Internet of Health Things environment for mitigating the security risks and preventing Internet of Health Things devices from becoming exposed to cyber-attacks. Artificial intelligence plays a primary role in anomaly detection. In this paper, a deep neural network-based cyber-attack detection system is built by employing artificial intelligence on latest ECU-IoHT dataset to uncover cyber-attacks in Internet of Health Things environment. The propos... [more]

A statistical design of experiments for a rotary fluidized bed agglomeration process is performed to improve both the knowledge of the process and the influence of the process parameters. Agglomerates of a pharmaceutical formulation are manufactured in a laboratory fluidized bed rotor apparatus with a tangential spray nozzle. Particle size is measured in-line over the entire agglomeration process with a spatial filter velocimetry probe installed directly in the process chamber and off-line with dynamic image analysis for comparison. The influence of the process parameters spray rate, spray pressure, rotor speed, and process air temperature on the fluidized bed is investigated using a central composite design. In-line measurement of particle size is possible over the entire rotor process. Spray pressure, spray rate, square of process air temperature, and some interactions proved to be statistically significant. Particle size measured with spatial filter velocimetry and dynamic image ana... [more]

This study proposes the design and application of wearable gloves that can recognize sign language expressions from input images via long short-term memory (LSTM) network models and can learn sign language through finger movement generation and vibration motor feedback. It is difficult for nondisabled people who do not know sign language to express sign language accurately. Therefore, we suggest the use of wearable gloves for sign language education to help nondisabled people learn and accurately express sign language. The wearable glove consists of a direct current motor, a link (finger exoskeleton) that can generate finger movements, and a flexible sensor that recognizes the degree of finger bending. When the coordinates of the hand move in the input image, the sign language motion is fed back through the vibration motor attached to the wrist. The proposed wearable glove can learn 20 Korean sign language words, and the data used for learning are configured to represent the joint coor... [more]

As one of the core pieces of equipment of the thermal power generation system, the economic and environmental performance of a boiler determines the energy efficiency of the thermal power generation unit. The oxygen content in boiler flue gas is an important parameter reflecting the combustion status of the furnace, and accurate prediction of flue gas oxygen content is of great significance for online boiler optimization. In order to solve the online prediction problem of the oxygen content in boiler flue gas, a CNN is applied to build a time series prediction model, which takes the time series samples within a fixed time window as the input of the model and uses several feature extraction modules containing convolutional, activation, and pooling layers for feature extraction and compression, and the model output is the oxygen content in boiler flue gas. Since the oxygen content in boiler flue gas is not only correlated with other variables but also influenced by its own historical tre... [more]

A non-Newtonian stagnation point fluid flow towards two different inclined heated surfaces is mathematically formulated with pertinent effects, namely mixed convection, viscous dissipation, thermal radiations, heat generation, and temperature-dependent thermal conductivity. Mass transfer is additionally considered by the use of a concentration equation. The flow narrating equations are solved numerically by using the shooting method along with the Runge−Kutta scheme. A total of 80 samples are considered for five different inputs, namely the velocities ratio parameter, temperature Grashof number, Casson fluid parameter, solutal Grashof number, and magnetic field parameter. A total of 70% of the data are used for training the network; 15% of the data are used for validation; and 15% of the data are used for testing. The skin friction coefficient (SFC) is the targeted output. Ten neurons are considered in the hidden layer. The artificial networking models are trained by using the Levenber... [more]

Due to the complexity of risk factors in constructing immersed tube tunnels, it is impossible to accurately identify risks. To solve this problem, and the uncertainty and fuzziness of risk factors, a risk assessment method for immersed tube tunnel construction was proposed based on WBS-RBS (Work Breakdown Structure-Risk Breakdown Structure), improved AHP (analytic hierarchy process), and cloud model theory. WBS-RBS was used to analyze the risk factors of immersed tube tunnel construction from the aspects of the construction process and 4M1E, and built a more comprehensive and accurate construction risk index system. The weight of each index was calculated by the improved AHP of a genetic algorithm. The cloud model theory was used to build the cloud map of risk assessment for immersed tunnel construction and evaluate construction risk. Taking the Dalian Bay subsea tunnel project as an example, the risk assessment method of immersed tunnel construction was verified. The results showed th... [more]

Increasing the efficiency of twin-screw machines is beneficial for gas compression and expansion applications. We used a computation fluid dynamic approach to obtain the flow field and efficiency of a twin-screw machine that used R-134a as the working fluid. The leakage flow and sealing lines were obtained to study their geometrical effects during the compression and expansion process. The effects of the wrap angle (280°, 290°, and 300°) and pressure ratios on the compression efficiency were studied. During the compression process, the volumetric efficiency was more than 70% regardless of the wrap angle. We found that the volumetric efficiency slightly decreased when the wrap angle increased. However, the effect of the wrap angle on the isentropic efficiency was not substantial. An increase in the pressure ratio decreased the mass flow rate and increased the leakage flow. This screw machine can also be operated in an expansion process, and the simulated expansion ratio was 3:1. However... [more]

Efforts to control climate change with the aim of achieving carbon neutrality by 2050 have had the most significant impact on businesses operating in the energy sector, which produce large amounts of greenhouse gas (GHG) emissions. In light of such policies, oil and gas companies have set goals aimed at reducing GHG emissions and achieving carbon neutrality, but the issue remains open as to how such activities and progress towards these goals can be evaluated. This study attempts to assess the activities and progress of oil and gas companies towards carbon neutrality, with a focus on quantitative evaluation of goal achievement. First, an algorithm was developed for selecting global oil and gas companies for the analysis that reported their activities in 2022. Using this algorithm, a list of companies was compiled and their goals with regard to carbon neutrality were analyzed. Second, an assessment of how information is presented in corporate reports and which activities aimed at achiev... [more]

The design of naval exhaust funnels has to take into account the interaction between the hot gases and topside structures, which usually includes critical electronic devices. Being able to predict the propagation trajectory, shape and temperature distribution of an exhaust gas plume is highly strategic in different industrial sectors. The propagation of a stack plume can be affected by different uncertainty factors, such as those related to the wind flow and gas flow conditions at the funnel exit. The constant growth of computational resources has allowed simulations to gain a key role in the early design phase. However, it is still difficult to model all the aspects of real physical problems in actual applications and, therefore, to completely rely upon the quantitative results of numerical simulations. One of the most important aspects is related to input variable uncertainty, which can significantly affect the simulation result. With this aim, the discipline of Uncertainty Quantific... [more]

Wind energy represents one of the leading renewable energy sectors and is considered instrumental in the ongoing decarbonization process. Accurate forecasts are essential for a reliable large-scale wind power integration, allowing efficient operation and maintenance, planning of unit commitment, and scheduling by system operators. However, due to non-stationarity, randomness, and intermittency, forecasting wind power is challenging. This work investigates a multi-modal approach for wind power forecasting by considering turbine-level time series collected from SCADA systems and high-resolution Numerical Weather Prediction maps. A neural architecture based on stacked Recurrent Neural Networks is proposed to process and combine different data sources containing spatio-temporal patterns. This architecture allows combining the local information from the turbine’s internal operating conditions with future meteorological data from the surrounding area. Specifically, this work focuses on multi... [more]

While large renewable power generation schemes, such as wind farms, are well monitored with a wealth of data provided through a SCADA system, the only information about the behaviour of small wind turbines is often only through the metered electricity production. Given the variability of electricity output in response to the local wind or radiation condition, it is difficult to ascertain whether particular electricity production in a metering period is the result of the system operating normally or if a fault is resulting in a sub-optimal production. This paper develops two alternative methods to determine a performance score based only on electricity production and proxy wind data obtained from the nearest available weather measurement. One method based on partitioning the data, consistent with a priori expectations of turbine performance, performs well in common wind conditions but struggles to reflect the effects of different wind directions. An alternative method based on Principal... [more]

The construction of smart grids has greatly changed the power grid pattern and power supply structure. For the power system, reasonable power planning and demand response is necessary to ensure the stable operation of a society. Accurate load prediction is the basis for realizing demand response for the power system. This paper proposes a Pre-Attention-CNN-GRU model (PreAttCG) which combines a convolutional neural network (CNN) and gate recurrent unit (GRU) and applies the attention mechanism in front of the whole model. The PreAttCG model accepts historical load data and more than nine other factors (including temperature, wind speed, humidity, etc.) as input. The attention layer and CNN layer effectively extract the features and weights of each factor. Load forecasting is then performed by the prediction layer, which consists of a stacked GRU. The model is verified by industrial load data from a German dataset and a Chinese dataset from the real world. The results show that the PreAt... [more]

In recent years, artificial intelligence has become increasingly popular and is more often used by scientists and entrepreneurs. The rapid development of electronics and computer science is conducive to developing this field of science. Man needs intelligent machines to create and discover new relationships in the world, so AI is beginning to reach various areas of science, such as medicine, economics, management, and the power industry. Artificial intelligence is one of the most exciting directions in the development of computer science, which absorbs a considerable amount of human enthusiasm and the latest achievements in computer technology. This article was dedicated to the practical use of artificial neural networks. The article discusses the development of neural networks in the years 1940−2022, presenting the most important publications from these years and discussing the latest achievements in the use of artificial intelligence. One of the chapters focuses on the use of artific... [more]

Failure modes, effects, and criticality analysis (FMECA) is a qualitative risk analysis method widely used in various industrial and service applications. Despite its popularity, the method suffers from several shortcomings analyzed in the literature over the years. The classical approach to obtain the failure modes’ risk level does not consider any relative importance between the risk factors and may not necessarily represent the real risk perception of the FMECA team members, usually expressed by natural language. This paper introduces the application of Type-I fuzzy inference systems (FIS) as an alternative to improve the failure modes’ risk level computation in the classic FMECA analysis and its use in cyber-power grids. Our fuzzy-based FMECA considers first a set of fuzzy variables defined by FMECA experts to embody the uncertainty associated with the human language. Second, the “seven plus or minus two” criterion is used to set the number of fuzzy sets to each variable, forming a... [more]

Reducing energy consumption and increasing operational efficiency are currently among the leading research topics in the design of hydraulic systems. In recent years, hydraulic system modeling and design techniques have rapidly expanded, especially using artificial intelligence methods. Due to the variety of algorithms, methods, and tools of artificial intelligence, it is possible to consider the prospects and directions of their further development. The analysis of the most recent publications allowed three leading technologies to be indicated, including artificial neural networks, evolutionary algorithms, and fuzzy logic. This article summarizes their current applications in the research, main advantages, and limitations, as well as expected directions for further development.

Different standard methods for the assessment of the thermal performance of the building envelope are used: analogy with coeval building, theoretical method, heat flow meter measurement, simple hot box, infrared thermography, and thermometric method. Review papers on these methods, applied in situ and in laboratory, have been published, focusing on theory, equipment, metrological performance, test conditions and data acquisition, data analysis, benefits, and limitations. However, steps forward have been done and not been deepened in previous works: in fact, the representative points method and the weighted area method have been proposed, too, whilst artificial intelligence and data-driven methods have begun to prove the reliability also in the U-value prevision using available datasets. Considering this context, this work aims at updating the literature background considering exclusively in situ methods. The work starts from bibliometric and scientometric analysis not previously conduc... [more]

In order to improve the safety and economy of nuclear reactors, it is necessary to analyze the sensitivity and uncertainty (S/U) of the nuclear data. The capabilities of S/U analysis has been developed in the Reactor Monte Carlo code RMC, using the iterated fission probability (IFP) method and the superhistory method. In this paper, the S/U capabilities of RMC are applied to a typical PWR benchmark B&W’s Core XI, and compared with the multigroup and continuous-energy S/U capabilities in the SCALE code system. The S/U results of the RMC-IFP method and the RMC-superhistory method are compared with TSUNAMI-CE/MG in SCALE. The sensitivity results and the uncertainty results of major nuclides that contribute a lot to the uncertainties in keff are in good agreement in both RMC and SCALE. The RMC-superhistory method has the same precision as the IFP method, but it reduces the memory footprint by more than 95% and only doubles the running time. The superhistory method has obvious advantages wh... [more]

Mining of minerals is an important part of the technical sciences, for which the certainty and unambiguity of terms and the correct application of definitions is an absolute requirement. At the same time, the expansion of Industry 4.0 technologies, both in practice and in scientific discussions, brings new terms to mining that are far from the original meaning. These terms include Data Mining and Mining 4.0, which, having a common digital core, refer to fundamentally different areas of human activity, and have the opposite meaning in relation to the use of resources (digital ones—endless, and the natural ones—finite). The indiscriminate use of the term “mining” is especially dangerous in the context of Mining 4.0, in which digital technologies allow the intensification of the exploitation of natural resources. This brief Perspective paper will show the role of terminology in Mining 4.0 and offer an interpretation of its relationship with Data Mining.

The computational models of physical systems comprise parameters, independent and dependent variables [...]