Predicting the dielectric strengths of organic compounds is critical for identifying potential insulating gases. However, experimental evaluation techniques are time-consuming, and current computational protocols are limited in scope. In this study, to develop a reliable prediction protocol for the dielectric strengths of a broad array of perfluorocarbon (PFC) and non-PFC compounds, systematic linear regression is combined with computational calculations of relevant core factors. The designed equation-based protocol is demonstrated to have four core factors, including two high-correlation factors (polarizability and molecular weight) and two critical factors (ionization energy and highest occupied molecular orbital (HOMO)–lowest unoccupied molecular orbital (LUMO) gap). The two critical factors are crucial for determining a suitable protocol, as reliable predictions of dielectric strength are only possible if the ionization energy and HOMO–LUMO gap are maintained within specified ranges for all the compounds. These findings can act as design guidelines for future computational protocols to predict the insulating properties of PFC and non-PFC compounds. Full article

We aimed to investigate the extent to which the initial state generated by unexpected loads, such as explosions and impacts on the remaining structure, affects the dynamic response of the structure. The study used the orthogonal test method to obtain orthogonal table L25 (53) by combining five levels of each of the three factors of column removal time and initial velocity, and the initial displacement of the remaining structure under three failure scenarios of the corner, side, and internal columns at the ground floor of the reinforced concrete frame structure. The degree of influence of different factors on the structural dynamic response, and the result of the unifactorial impact of initial velocity and initial displacement of the remaining structure on the structural dynamic response in the case of failure of the bottom side columns, were obtained by the polar difference method. The results show that the analysis using the orthogonal test polar difference method revealed that the initial displacement has a more significant influence on the dynamic response of the structure, forming the main influencing factor. At the same time, the failure time and initial velocity have a smaller influence on the dynamic response of the structure as secondary influencing factors. In the case of unifactorial influence, the initial upward displacement and initial upward velocity are detrimental to the structure, leading to progressive collapse. In contrast, the initial downward velocity and initial downward displacement are favorable. Full article

Point absorber wave energy converter (WEC) control strategies often require accurate models for maximum energy extraction. While linear models are suitable for small motions, the focus is on the nonlinear model of an hour-glass shaped buoy undergoing large vertical displacements. Closed-form expressions for the static and dynamic Froude–Krylov forces are developed. It is shown that, in general, the dynamic and static forces are of similar magnitude, which is not the case for a spherical buoy. While the dynamic force reduces the amplitude of the net buoy force, its shape predicts a larger buoy response than if neglected, causing the nonlinear terms to have an even more significant effect. An input-state feedback linearizing controller is developed to show how the nonlinear model can be used in a control law. A 2.5 m buoy example is simulated to illustrate the approach of tracking an arbitrary displacement reference. For the case considered, the extracted power is 30% larger when the nonlinear dynamic FK force is used in the control law. The hourglass buoy is also compared to a spherical buoy to illustrate differences in their response to regular waves and energy extraction when using the same control laws. A spherical buoy diameter of 7.5 m was required to obtain the same power output as a 5 m tall hourglass buoy. A power-force-amplitude (PFA) metric is introduced to compare energy extraction performance and power take-off requirements. The hourglass buoy’s PFA was 13% larger than the spherical buoy implying that it can produce similar power but with less control effort. Full article

With the continuous development of business intelligence technology, the application research of decision support systems (DSSs) is deepening. In China, the work in this area started relatively late, and there are few DSS research cases to assist in marketing decision-making. Currently, marketing decision support systems have shortcomings in data integration, historical data, query functions, and data analysis. This article analyzes the characteristics of marketing decision-making, discusses the application of data warehouse, OLAP, and data mining technology in marketing decision support systems, and designs a marketing decision support system based on data mining technology. The system uses a BP neural network to conduct data mining marketing forecasting. A three-layer network model for marketing prediction is established, with sales time, product price, and customer purchasing power as network inputs and output as the sales volume of a certain type of product in different locations. The test results show that the average absolute percentage error of this method is 15.13%, and the prediction accuracy is high. Research shows that with the continuous development of data mining technology, the system cannot only help users conduct scientific and reasonable marketing decision-making analyses, making the marketing decision-making process more scientific and reasonable, but also can bring new ideas to enterprise decision-makers, promoting the continuous improvement and progress of the system. Full article

Gingipains (RgpA, RgpB, and Kgp) are major virulence factors of the periodontitis-causing bacterium Porphyromonas gingivalis. Isolation of gingipains from the crude protein sample of P. gingivalis is critical for studying the underlying invasion mechanism that contributes to periodontitis, Alzheimer’s disease, and cardiovascular disease (CVD). Chromatographic processes and molecular cloning are two standard techniques often used for gingipains isolation, which are time-consuming and costly. In this study, considerably easier methods based on passive-mediated diffusion gel elution and gelatin zymogram were used to isolate and characterize gingipains. Importantly, proteins eluted from Native-PAGE showed enzymatic activity for both Rgp and Kgp. In gelatin zymography, the proteins with a molecular size of ~50 kDa and above 245 kDa were suggested as arginine-specific gingipains. The passive diffusion-mediated gel elution method is a simpler technique to isolate gingipains from crude protein samples of P. gingivalis. By using covalent and highly specific gingipain inhibitors, gelatin zymography enabled an individual characterization of gingipain activity and inhibition. Finally, this protocol can be easily extended by adding the isoelectric focusing to further improve the protein separation and characterization. Full article

Reactive multilayer foils (RMFs) for joining processes have attracted a great deal of attention over the last few years. They are capable of exothermic self-propagating reactions and can serve as localized heat sources for joining applications when ignited by suitable means. Using short and ultrashort pulsed lasers with carefully selected parameters, cutting and shaping of RMFs makes it possible to tailor heat release characteristics without triggering the reaction. The present study is an investigation of microstructural changes induced by femtosecond laser machining of a commercially available Ni/Al-based RMF. The effects of the specific laser parameters pulse duration and repetition rate on the heat-affected zone (HAZ) are investigated by scanning and transmission electron microscopy. Debris consisting of oxide deposits can be found at a distance of several tens of microns from the cut edge. A negligible HAZ extending to less than 100 nm was observed for all parameters tested and no signs of ignition of a self-propagating reaction were observed. These results underline the suitability of femtosecond lasers for metal machining with minimal heat input. Full article

The films teenagers watch have a significant influence on their behavior. After witnessing a film starring an actor with a particular social habit or personality trait, viewers, particularly youngsters, may attempt to adopt the actor’s behavior. This study proposes an algorithm-based technique for predicting the market potential of upcoming science fiction films. Numerous science fiction films are released annually, and working in the film industry is both profitable and delightful. Before the film’s release, it is necessary to conduct research and make informed predictions about its success. In this investigation, different machine learning methods written in MATLAB are examined to identify and forecast the future performance of movies. Using 14 methods for machine learning, it was feasible to predict how individuals would vote on science fiction films. Due to their superior performance, the fine, medium, and weighted KNN algorithms were given more consideration. In comparison to earlier studies, the KNN-adopted methods displayed greater precision (0.89–0.93), recall (0.88–0.92), and accuracy (90.1–93.0%), as well as a rapid execution rate, more robust estimations, and a shorter execution time. These tabulated statistics illustrate that the weighted KNN method is effective and trustworthy. If several KNN algorithms targeting specific viewer behavior are logically coupled, the film business and its global expansion can benefit from precise and consistent forecast outcomes. This study illustrates how prospective data analytics could improve the film industry. It is possible to develop a model that predicts a film’s success, effect, and social behavior by assessing features that contribute to its success based on historical data. Full article

Aqueous formulations based on anionic butadiene-styrene microspheres (BSMs) and cationic poly(diallyldimethylammonium chloride) (PDADMAC) with the weight PDADMAC fraction from 0 to 1 were studied as the stabilizers of loose sandy soils. In general, these systems were shown to be represented as the mixtures of microspheres saturated with PDADMAC and unbound polycation. Mechanical testing of BSMs–PDADMAC films evidenced that with increasing weight PDADMAC fraction, a 20-fold growth in elastic modulus, 2-fold growth in strength and 2-fold decrease in ultimate strain of the material were observed. Treatment of the sand with the above formulations resulted in formation of a protective porous polymer-sand surface crust with the strength from 0.8 to 45.0 MPa. “Elasticity–rigidity” balance and water resistance of the crusts were controlled by weight fraction of polycation in the mixed formulation. Stable water-resistant polymer-sand crusts were shown to be prepared using formulations with the weight PDADMAC fraction from 0 to 0.2. The results indicated a great potential of the polymer-colloid formulations for the fabrication of structured sand coatings with controlled properties. Full article

In most machine learning (ML) applications, data that arrive from heterogeneous views (i.e., multiple heterogeneous sources of data) are more likely to provide complementary information than does a single view. Hence, these are known as multi-view data. In real-world applications, such as web clustering, data arrive from diverse groups (i.e., sets of features) and therefore have heterogeneous properties. Each feature group is referred to as a particular view. Although multi-view learning provides complementary information for machine learning algorithms, it results in high dimensionality. However, to reduce the dimensionality, feature selection is an efficient method that can be used to select only the representative features of the views so to reduce the dimensionality. In this paper, an unsupervised feature selection for online dynamic multi-views (UFODMV) is developed, which is a novel and efficient mechanism for the dynamic selection of features from multi-views in an unsupervised stream. UFODMV consists of a clustering-based feature selection mechanism enabling the dynamic selection of representative features and a merging process whereby both features and views are received incrementally in a streamed fashion over time. The experimental evaluation demonstrates that the UFODMV model has the best classification accuracy with values of 20% and 50% compared with well-known single-view and multi-view unsupervised feature selection methods, namely OMVFS, USSSF, and SPEC. Full article

Flyers driven by various technologies have been widely used as effective shock loading techniques in the ignition of energetic materials, space debris impact modeling, equation of state measurement, etc. However, their flying characteristics have the greatest influence on application reliability. The flying velocity, as well as the flyer’s planarity and integrity, are crucial characteristics. Therefore, mainstream flyer-driving technologies are examined in this study, and research progress in investigation approaches on flyer velocity and morphology are reviewed. Furthermore, for each flyer-driving technique, the affecting factors of flyer velocity and morphology are summarized. Current research deficiencies are highlighted, and future research directions are discussed, including the parallel coupling of various driving principles, quantitative characterizations of the flyer’s planarity and integrity, and the physical model matching the flying process. Full article

Pumps, as core pieces of equipment in ships, are installed in the engine room to supply refined oil to the engine. Pump failure causes critical problems for ship operations. Therefore, failure-monitoring-based diagnosis technology is an essential requirement in the shipbuilding industry. For this purpose, a database containing information about the failure states depending on the main cause of the failure cases of the pump needs to be developed. In the present study, failure causes of pumps based on actual accident records were quantitatively analyzed. Then, failure modes for the bearing, coupling, sealing, and screw, which are the core parts of the oil pump, were determined. Test infrastructures for the oil pump were developed to obtain normal and abnormal data considering diverse operating conditions. Based on the vibration data from the accelerometer installed on the test infrastructures, the frequency of failure was analyzed through Fast Fourier Transform (FFT). In addition, more precise results were obtained by performing Short-Time Fourier Transform (STFT) for the FFT results that indicated severe failure. Finally, over 200 data entries were accumulated on the core parts of the oil pump, considering normal as well as abnormal operating conditions. The database constructed in this study is expected to help in investigating failure diagnosis and prediction of algorithm models for ship management. Full article

Vessel trajectory prediction supports navigation services and collision detection. To maintain safety and efficiency in maritime transportation, vessel trajectory prediction is always an important topic. By using automatic identification system (AIS) data and deep learning methods, the task of vessel trajectory prediction has made significant progress. However, this task is still full of challenges due to the complexity of historical information dependencies and the strong influence of spatial correlations. In this paper, we introduce a novel deep learning model, PESO, based on the structure of Seq2Seq, consisting of Parallel Encoders and a Ship-Oriented Decoder. The Parallel Encoders, including the Location Encoder and the Sailing Status Encoder are designed to integrate more information into feature representation. The Ship-Oriented Decoder is targeted to utilize the Semantic Location Vector (SLV) to guide the prediction, which better represents the spatial correlation of historical track points. In order to verify the efficiency and efficacy of PESO, we conducted comparative experiments with several baseline models. The experimental results demonstrate that PESO is superior to them both quantitatively and qualitatively. Full article

This paper introduces a novel type of buckling restrained braces (BRBs) called core-separated assembled BRBs (CSA-BRBs). These braces are comprised of two single BRBs that are confined by lightweight concrete-infilled tubes, which are longitudinally connected by two continuous webs. The CSA-BRBs utilize materials more efficiently by increasing the height of the webs to create a large inner cavity, leading to an economical design. This paper predicts the threshold of the restraint ratio of CSA-BRBs approximately. This is achieved by assuming that the maximum moment resulting from applied loads at mid-height is less than the moment-bearing resistance that is conducted according to the outermost fiber of the external restraining section reading yielding. Elastic-plastic numerical analysis is conducted using FEM with beam elements for CSA-BRBs that are subjected to both monotonic and cyclic axial loads. The load resistance, hysteretic performance, and failure mechanism of CSA-BRBs are investigated by varying their restraining ratios. It is recommended that the restraint ratio threshold of CSA-BRBs under monotonic axial compression is used as a bearing type and the restraint ratio threshold of CSA-BRBs under axially compressive-tensile cyclic loads as an energy-dissipation type. This method provides a complete design for CSA-BRBs. Full article

Unsaturated soil shear strength is a significant topic in geotechnical engineering. The measurement of unsaturated soil properties such as shear strength and matric suction could be costly, hard, time-consuming, and often impractical to obtain. The purpose of this research is to propose a new shear strength model for unsaturated soil and to predict the shear strength by using the degree of saturation directly because the water saturation is easy to obtain. More specifically, this study focused on Bishop’s shear strength theory and van Genuchten’s soil–water characteristic curve to describe the effect of saturation on shear strength. The new shear strength model was expressed as a function of saturation degree, residual degree of saturation, effective shear strength indices, net normal stress, and five parameters. The performance of the presented model in this paper was verified by fitting it to data obtained by laboratory tests on silty sand including the triaxial shear test and soil–water characteristic curve test. From these two laboratory tests, we obtained the variation in the matric suction and shear strength with the degree of saturation under various dry densities. This proposed model was also validated against the shear strength results of the clayed-silty sand and Ankara clay recorded in the literature. Comparing the result of the shear strength under the variation of one parameter, this model was much more sensitive regarding parameter m, which was related to the material characteristic. The comparison between the predicted values and experimental points was particularly fine and showed the ability of this model to be applied to a wide range of soils. Full article

In the economically developed Beijing–Tianjin–Hebei region, magnetotelluric data are susceptible to contamination from cultural noise, which can be caused, for example, by urban stray currents, high-speed railways, or high-voltage lines. The multiple remote references method is an effective tool that can be used to suppress interference and improve signal-to-noise ratios. Therefore, this paper first introduces the basic principles of multiple remote references and then takes high-speed railway noise as an example. The characteristics of the time domain and frequency domain of the high-speed railway noise signals are analyzed. Then, we use two remote reference stations (with a single remote reference and multiple remote references) to process the data interfering with the high-speed railway and compare the results. Finally, the multiple remote references method is used to process the data for the entire section. Coupled with the known geological and seismic data, the inversion results well-reflect the deep underground geological structure. Full article